To see the other types of publications on this topic, follow the link: Skin cancer detection.
Dissertations / Theses on the topic 'Skin cancer detection'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 32 dissertations / theses for your research on the topic 'Skin cancer detection.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
1
Moustafa, Ahmed. "Skin cancer Detection byTemperature VariationAnalysis." Thesis, KTH, Skolan för teknik och hälsa (STH), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-107422.
Full textAbstract:
In the medical field new technologies are incorporated for the sole purpose to enhance the quality of life for patients and even for the normal persons. Infrared technology is one of the technologies that has some applications in both the medical and biological fields. In this work, the thermal infrared (IR) measurement is used to investigate its potential in skin cancer detection. IR enjoys a non-invasive and non-contact advantages as well as favorable cost, apparently. It is also very well developed regarding the technological and methodological aspects. IR radiation, per se, is an electromagnetic radiation that all objects emit when their temperature is above the absolute zero. Human body is not different. The IR range extends, ideally, to cover wavelengths from 800 nanometer to few hundreds micrometer. Cancer, in modern life, has grown tangibly due to many factors apparently such life expectancies increase, personal habits, and ultraviolet radiation (UV) exposures among others. Moreover, the significant enhancement of technologies has helped identifying more types of cancers than before. The purpose of this work is to investigate further another method and application of IR technology not yet matured in detection of skin cancer to enhance detection ability that is accompanied with higher level of safety. An extensive research project was designed to use two laboratory animals injected with cancer cells subcutaneously and two IR radiation sensors able to detect wavelengths in the range 8 – 14 μm which proved to be a favorable range to measure the temperature of the skin. Data collection performed using two lab animals as subjects that formed a double blind investigation process. An analysis of the observations was conducted both in qualitative as well as quantitative approaches. The analysis and discussion revealed the potential of the thermal IR radiation in detecting skin cancer existence. The thesis was supported with significant evidence and achieved its target. Furthermore, it was clear that the functional nature of thermal IR detection constitutes another advantage for this method that can be used in the future to develop an objective and automated method for detection of skin cancer in a straight forward and cost effective manner.
2
Baldwin, Angela Michelle. "Mueller matrix imaging for skin cancer detection." Texas A&M University, 2004. http://hdl.handle.net/1969.1/340.
Full textAbstract:
Over one million Americans are afflicted with skin cancer each year. Even though skin cancer has a 95% cure rate, approximately 10,000 people die in the United States each year of this disease. The current ABCDE(F) detection method is not sensitive enough to detect skin cancer in its early stages and requires a biopsy for any suspicious lesions. A lot of unnecessary biopsies, which are painful and costly to the patient, are taken. Therefore, a noninvasive technique is needed that can accurately detect the presence of skin cancer. In this thesis, an optical approach will be presented that has potential to be a noninvasive skin cancer detection technique. Several morphological and biochemical changes occur as tissue becomes cancerous, and therefore the optical properties of the tissue can be used to detect skin cancer. A Mueller matrix imaging system has been developed by our group that measures the 16 or 36-element Mueller matrix, which completely describes the optical properties of the tissue sample. The system is automated and can collect the Mueller matrix in less than one minute. This system will be used to
image Sinclair swine, and data analysis techniques will be employed to determine if the system can distinguish between cancerous and noncancerous tissue. System software improvements will also be made, and a new calibration technique will be presented.
3
Munnangi, Anirudh. "Innovative Segmentation Strategies for Melanoma Skin Cancer Detection." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1510916097483278.
Full text
4
Smith, Elizabeth Brooks. "Skin cancer detection by oblique-incidence diffuse reflectance spectroscopy." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1047.
Full text
5
Quintana, Plana Josep. "Computer vision techniques for early detection of skin cancer." Doctoral thesis, Universitat de Girona, 2012. http://hdl.handle.net/10803/82072.
Full textAbstract:
This thesis investigates the problem of developing new computer vision techniques for early detection of skin cancer. The first part of this work presents a novel methodology to correct color reproduction in dermatological images when different cameras and/or dermoscopes are used. Next, the problem of automatic full body mapping is addressed by proposing a mosaicing method based on an on-the-shelf digital compact camera and a set of markers. This method increases the possibilities of total body photography by taking the low-resolution images of a whole body exploration and automatically combining them into a high-resolution photomosaic. The third contribution of this work consists of the development of a full body scanner for acquiring cutaneous images. On one hand, the scanner reduces the long time-consuming examinations done in dermoscopy explorations, and on the other hand, it increases the resolution of total body photography systems.En aquesta tesi s'investiga el desenvolupament de noves tècniques de visió per computador per a la detecció del càncer de pell. La primera part del treball presenta una nova metodologia per a la correcció del color en imatges dermatològiques quan s'utilitzen diferents càmeres i/o els dermatoscops. A continuació és proposa una solució al problema del registre automàtic d'imatges de cos complert amb la proposta d’un mètode de mosaicing basat en l'ús de càmeres compactes i un conjunt de markers. Incrementant les possibilitats de la fotografia de cos complert mitjançant la combinació automàtica d’imatges de baixa resuloció per a l'obtenció d'un fotomosaic d’alta resolució. La tercera contribució d'aquest treball consisteix en el desenvolupament d'un escàner de cos complert per a l'adquisició d'imatges cutànies. D'una banda l'escàner redueix el llarg temps necessari per a les exploracions dermatoscòpiques, i de l'altre, incrementa la resolució de la fotografia de cos complet.
6
Cheung, Karen. "Image processing for skin cancer detection, malignant melanoma recognition." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq29403.pdf.
Full text
7
Barham, S. Y. "Time series analysis in the detection of breast cancer." Thesis, Bucks New University, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.384665.
Full text
8
Bränström, Richard. "Skin cancer prevention : behaviours related to sun exposure and early detection /." Stockholm, 2003. http://diss.kib.ki.se/2003/91-7349-550-6/.
Full text
9
Poma, Jonathan Miguel Campos, Emily Yanira De La Cruz Dominguez, Jimmy Armas-Aguirre, and Leonor Gutierrez Gonzalez. "Extended Model for the Early Skin Cancer Detection Using Image Processing." IEEE Computer Society, 2020. http://hdl.handle.net/10757/656579.
Full textAbstract:
El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.In this research paper, we proposed an extended model for the early detection of skin cancer... The purpose is reduce the waiting time to obtaining a diagnosis, in addition, the function of the dermatoscope has been digitized by using a Smartphone and magnifying lenses as an accessory the mobile device. The proposed model has five phases: 1. The patient is attended by a general practitioner or nurse previously trained in any health center which has WiFi or mobile network connectivity to record their data and capture the skin lesion that will be analyzed. 2) The image will be in the cloud storage, which at the same time feeds an exclusive access website of dermatologists.3) Images are analyzed in real time using an image recognition service provided by IBM, which is integrated into a cloud-hosted web platform and an-Android application. 4)The result of the image processing is visualized by the dermatologist who makes a remote diagnosis.5) This diagnosis is received by the general practitioner or nurse, responsible for transmitting the diagnosis and treatment to the patient. This model was validated in a group of 60 patients, where 28 suffer from skin cancer in the early stage, 12 in the late stage and 20 are healthy patients, in a network of clinics in Lima, Peru. The obtained result was 97.5% of assertiveness on the analyzed skin lesions and 95% in healthy patients.
Revisión por pares
10
Almasiri, osamah A. "SKIN CANCER DETECTION USING SVM-BASED CLASSIFICATION AND PSO FOR SEGMENTATION." VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5489.
Full textAbstract:
Various techniques are developed for detecting skin cancer. However, the type of maligned skin cancer is still an open problem. The objective of this study is to diagnose melanoma through design and implementation of a computerized image analysis system. The dataset which is used with the proposed system is Hospital Pedro Hispano (PH²).
The proposed system begins with preprocessing of images of skin cancer. Then, particle swarm optimization (PSO) is used for detecting the region of interest (ROI). After that, features extraction (geometric, color, and texture) is taken from (ROI). Lastly, features selection and classification are done using a support vector machine (SVM).
Results showed that with a data set of 200 images, the sensitivity (SE) and the specificity (SP) reached 100% with a maximum processing time of 0.03 sec.
11
Adjed, Faouzi. "Skin cancer segmentation and detection using total variation and multiresolution analysis." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLE042/document.
Full textAbstract:
Les décès du cancer de la peau sont majoritairement des mélanomes malins. Il est considéré comme l’un des plus dangereux cancer. A ses débuts, les mélanomes malins sont traités avec des simples biopsies et sont complètement curable. Pour cela, une détection précoce est la meilleure solution pour réduire ses conséquences désastreuses. Imagerie médicale telle que la dermoscopie et les caméras à images standard sont les outils disponibles les plus adaptées pour diagnostiquer précocement les mélanomes. Le diagnostic assisté par ordinateur (CAD) est développé dans le but d’accompagner les radiologistes dans la détection et le diagnostic. Cependant, il y a un besoin d’améliorer la précision de la segmentation et de détection des lésions. Dans ce travail, le modèle de Chan et Vese a été adapté pour segmenter davantage les variations à l’intérieur des lésions avec un résultats très encouragent. La deuxième tâche consiste à extraire des caractéristiques afin de discriminer les mélanomes. Deux méthodes ont été développée, une se basant sur l’irrégularité des bords des lésions et l’autre par la fusion des caractéristiques texturales et structurelles. Les résultats ont montrés de bonnes performances avec une précision de 86.54% et de 86.07%, respectivementThe vast majority of skin cancer deaths are due to malignant melanoma. It is considered as one of the most dangerous cancers. In its early stages, malignant melanoma is completely curable with a simple biopsy. Therefore, an early detection is the best solution to improve skin cancer prognostic. Medical imaging such as dermoscopy and standard camera images are the most suitable tools available to diagnose melanoma at early stages. To help radiologists in the diagnosis of melanoma cases, there is a strong need to develop computer aided diagnosis (CAD) systems. The accurate segmentation and classification of pigment skin lesions still remains a challenging task due to the various colors and structures developed randomly inside the lesions. The current work focused on two main tasks. Inthe first task, a new approach of the segmentation of skin lesions based on Chan and Vese model is developed. The model is adapted to segment the variations of the pigment inside the lesion and not only the main border. The subjective evaluation, applied on a database of standard camera images, obtained a very encouraging results with 97.62% of true detection rate. In the second main task, two feature extraction methods were developed for the analysis of standard camera and dermoscopy images. The method developed for the standard camera skin cancer images is based on border irregularities, introducing two new concepts, which are valleys and crevasses as first and second level of the border irregularity. The method has been implemented on DermIs and DermQues, two databases of standard camera images, and achieved an accuracy of 86.54% with a sensitivity of 80% and a specificity of 95.45%. The second method consisted of a fusion of structural and textural features. The structural features were extracted from wavelet and curvelet coefficients, while the textural features were obtained from the local binary pattern operator. The method has been implemented on the PH2 database for dermoscopy images with 1000-random sampling cross validation. The obtained results achieved an accuracy, a sensitivity and a specificity of 86:07%, 78.93% and 93.25%. Compared to the existing methods, the proposed methods in this work show very good performances
12
Gu, Yanyang. "Melanoma Detection Based on Color and Hyperspectral Imaging." Thesis, Griffith University, 2019. http://hdl.handle.net/10072/386570.
Full textAbstract:
Melanoma is the most fatal form of all skin cancer types, being diagnosed mostly among lightly pigmented skin. An early screening of melanoma can greatly contribute to successful treatment, hence reliable early detection systems are highly demanded and key technologies need to be developed.
The existing automated melanoma detection algorithms are dominantly based on color images. Early methods adopted machine vision algorithms which require hand-crafted features to be designed. With the development of machine learning models and the
access to the large skin image datasets, deep learning has been introduced for melanoma detection so effective feature can be automatically learned. However, the research on this topic is at its early stage.
While machine learning models can be created for skin cancer detection, this task can also be boosted by introducing new imaging technology beyond the traditional color imaging process. To this end, hyperspectral images show its advantage because of their multiple spectral bands, thereby providing extra reflectance information that is related to the intrinsic properties of skins and its composition. The challenges on this technology are that there is no open dataset to support the research and how to effectively use the spectral and spatial information in the hyperspectral images for melanoma detection
remains unsolved. To address the above issues, in this thesis, we introduce three methods for melanoma detection.
The first method is based on machine vision. This method follows the common image classifi cation pipeline, i.e. pre-processing, segmentation, feature extraction, and classifi cation. The novelty of this method is that before classifi cation, we introduce a dimensionality reduction method to the extracted features as a post-processing step. This post-processing procedure is based on Mahalanobis distance learning and constrained graph regularized nonnegative matrix factorization. The proposed method allows supervised learning for feature dimensionality reduction by incorporating both global geometry
and local manifold, so as to enhance the discriminability of the classifi cation performance. The proposed method is evaluated on PH2 Dermoscopy Image Dataset and Edinburgh Dermofi t Image Library, with comparison against four alternative classifi cation methods. The experimental results demonstrate that the best performance is achieved with the proposed method compared with another NMF baseline method and
direct classifi cation without post-processing.
The second melanoma detection method is deep learning-based. Deep learning is a datadriven
technique that does not require hand-crafted feature design, thereby improving
the generalization capacity of the model. However, a well-trained deep learning model
from one dataset often cannot be generalized to other datasets, even when all datasets
have the same categories. This is mainly because of the domain shift between datasets
of different cohorts in the data capture process. On this regards, we exploit two methods
to relieve this issue by evaluating on two different skin disease datasets, MoleMap and
HAM10000. The fi rst option is parameter-based transfer learning. We use a progressive
transfer learning scheme to share transferable knowledge between multiple datasets, i.e.
transferring knowledge from a task-different source dataset (ImageNet) to a category-same
but dataset-different intermediate dataset (MoleMap) and at last to the target
dataset (HAM10000). For the second option, we use cycle-consistent generative adversarial
networks to translate the images from the source domain into the target domain
for pixel-wise image adaptation. The synthesized image data are integrated with the
training samples in the original target domain during the training stage, therefore forming
the methods of dataset adaptation and modality domain adaptation. The results of
progressive transfer learning show that it achieves better performance and generalization
capacity than 1-step transfer learning model and model training from scratch. Furthermore,
both dataset adaptation and modality domain adaptation show improvements of
the model generalization capacity, melanoma detection, skin cancer detection, and skin
disease classi fication.
The third method is by means of hyperspectral imaging. Besides the spatial information,
hyperspectral imaging provides fine resolution in spectral wavelength. With the
abundant spectral-spatial information, hyperspectral imaging can facilitate melanoma
detection. In this research, we introduce a hyperspectral dermoscopic dataset and describe
a detailed description of the hardware and software of the data collection system
developed in the Spectral Imaging Lab of Griffth University. As far as we know, this is
the fi rst open hyperspectral dermoscopic benchmark dataset. Based on this dataset, we
provide the baselines using machine learning methods, which include sparse coding, support
vector machine, and deep learning. We show the performance of spatial features,
spectral features and joint spectral-spatial features on this dataset. The experiments
show that the classifi cation performance is improved with extra spectral features.Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
Full Text
13
Azzarello, Lora M. "Psychological Factors Associated with Skin Cancer Detection Behaviors in Individuals with a Family History of Melanoma." [Tampa, Fla.] : University of South Florida, 2003. http://purl.fcla.edu/fcla/etd/SFE0000174.
Full text
14
Kretzler, Madison Elizabeth. "AUTOMATED CURVED HAIR DETECTION AND REMOVAL IN SKIN IMAGES TO SUPPORT AUTOMATED MELANOMA DETECTION." Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1365125074.
Full text
15
Conover, Susan (Susan Teresa). "Prime areas for improvement in skin cancer detection and how technology can help." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/105308.
Full textAbstract:
Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, Engineering Systems Division, 2015.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 138-148).
About 5 million cases of skin cancer will be diagnosed in the United States in 2015, making skin cancer the most common cancer diagnosis in the United States. About 13,000 Americans will die from skin cancer in 2015. Often skin cancers are diagnosed at later stages, are expensive to treat, and result in fatalities. For melanoma, responsible for 75% of skin cancer deaths, the overall 5-year survival rate is 98% for skin lesions detected in their early stages, and this survival rate drops to 16% after the disease has spread to other organs. If these fatal skin cancers were detected earlier they would cost less to treat and result in better patient outcomes. There is no single resource available that maps the full state of the skin cancer care delivery, and most current views are colored by a stakeholder's perspective. We connected with stakeholders at different levels of the skin cancer care delivery system to create an overall picture of the system's current state and to identify gaps in care. We interviewed 9 skin cancer patients, 8 primary care physicians, and 9 dermatologists. Through this research, we discovered that the structure of how skin cancer care is delivered promotes opportunities to miss skin cancers and includes many barriers between initial cancer suspicion and disease diagnosis. Frequently patients do not evaluate themselves for skin cancer, primary care physicians have low accuracy in identifying skin cancers, and dermatologists manage a very small portion of the population who develop skin cancers. At a higher level, feedback between patients and physicians is frequently lost in the system, physicians are not accountable for patient outcomes, and patient health is not supported by the system until the patient identifies a health issue and acts to remedy the issue. To close these system gaps, we identified technologies, including micro-biopsies and electrical impedance spectrometry, which could be used to improve rates of skin cancer identification and promote better patient health outcomes. Additionally, we recommend physicians find a way to collaborate on cases, identify their own weaknesses in assessment, and capture patient outcomes to relay incorrect assessments to other physicians to improve future patient care.
by Susan Conover.
S.M. in Engineering and Management
16
Papanastasiou, Maria. "Use of Deep Learning in Detection of Skin Cancer and Prevention of Melanoma." Thesis, KTH, Skolan för teknik och hälsa (STH), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209007.
Full textAbstract:
Melanoma is a life threatening type of skin cancer with numerous fatal incidences all over the world. The 5-year survival rate is very high for cases that are diagnosed in early stage. So, early detection of melanoma is of vital importance. Except for several techniques that clinicians apply so as to improve the reliability of detecting melanoma, many automated algorithms and mobile applications have been developed for the same purpose.In this paper, deep learning model designed from scratch as well as the pretrained models Inception v3 and VGG-16 are used with the aim of developing a reliable tool that can be used for melanoma detection by clinicians and individual users. Dermatologists who use dermoscopes can take advantage of the algorithms trained on dermoscopical images and acquire a confirmation about their diagnosis. On the other hand, the models trained on clinical images can be used on mobile applications, since a cell phone camera takes images similar to them.The results using Inception v3 model for dermoscopical images achieved accuracy 91.4%, sensitivity 87.8% and specificity 92.3%. For clinical images, the VGG-16 model achieved accuracy 86.3%, sensitivity 84.5% and specificity 88.8%. The results are compared to those of clinicians, which shows that the algorithms can be used reliably for the detection of melanoma.
17
Viana, Joaquim Mesquita da Cunha. "Classification of skin tumours through the analysis of unconstrained images." Thesis, De Montfort University, 2009. http://hdl.handle.net/2086/2400.
Full textAbstract:
Skin cancer is the most frequent malignant neoplasm for Caucasian individuals. According to the Skin Cancer Foundation, the incidence of melanoma, the most malignant of skin tumours, and resultant mortality, have increased exponentially during the past 30 years, and continues to grow. [1]. Although often intractable in advanced stages, skin cancer in general and melanoma in particular, if detected in an early stage, can achieve cure ratios of over 95% [1,55]. Early screening of the lesions is, therefore, crucial, if a cure is to be achieved. Most skin lesions classification systems rely on a human expert supported dermatoscopy, which is an enhanced and zoomed photograph of the lesion zone. Nevertheless and although contrary claims exist, as far as is known by the author, classification results are currently rather inaccurate and need to be verified through a laboratory analysis of a piece of the lesion’s tissue. The aim of this research was to design and implement a system that was able to automatically classify skin spots as inoffensive or dangerous, with a small margin of error; if possible, with higher accuracy than the results achieved normally by a human expert and certainly better than any existing automatic system. The system described in this thesis meets these criteria. It is able to capture an unconstrained image of the affected skin area and extract a set of relevant features that may lead to, and be representative of, the four main classification characteristics of skin lesions: Asymmetry; Border; Colour; and Diameter. These relevant features are then evaluated either through a Bayesian statistical process - both a simple k-Nearest Neighbour as well as a Fuzzy k-Nearest Neighbour classifier - a Support Vector Machine and an Artificial Neural Network in order to classify the skin spot as either being a Melanoma or not. The characteristics selected and used through all this work are, to the author’s knowledge, combined in an innovative manner. Rather than simply selecting absolute values from the images characteristics, those numbers were combined into ratios, providing a much greater independence from environment conditions during the process of image capture. Along this work, image gathering became one of the most challenging activities. In fact several of the initially potential sources failed and so, the author had to use all the pictures he could find, namely on the Internet. This limited the test set to 136 images, only. Nevertheless, the process results were excellent. The algorithms developed were implemented into a fully working system which was extensively tested. It gives a correct classification of between 76% and 92% – depending on the percentage of pictures used to train the system. In particular, the system gave no false negatives. This is crucial, since a system which gave false negatives may deter a patient from seeking further treatment with a disastrous outcome. These results are achieved by detecting precise edges for every lesion image, extracting features considered relevant according to the giving different weights to the various extracted features and submitting these values to six classification algorithms – k-Nearest Neighbour, Fuzzy k-Nearest Neighbour, Naïve Bayes, Tree Augmented Naïve Bayes, Support Vector Machine and Multilayer Perceptron - in order to determine the most reliable combined process. Training was carried out in a supervised way – all the lesions were previously classified by an expert on the field before being subject to the scrutiny of the system. The author is convinced that the work presented on this PhD thesis is a valid contribution to the field of skin cancer diagnostics. Albeit its scope is limited – one lesion per image – the results achieved by this arrangement of segmentation, feature extraction and classification algorithms showed this is the right path to achieving a reliable early screening system. If and when, to all these data, values for age, gender and evolution might be used as classification features, the results will, no doubt, become even more accurate, allowing for an improvement in the survival rates of skin cancer patients.
18
Yuan, Ye. "Investigation of Skin and Skin Components Using Polarized Fluorescence and Polarized Reflectance Towards the Detection of Cutaneous Melanoma." University of Toledo / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1147284058.
Full text
19
Adhikari, Aakriti. "Skin Cancer Detection using Generative Adversarial Networkand an Ensemble of deep Convolutional Neural Networks." University of Toledo / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1574383625473665.
Full text
20
Aldridge, Roger Benjamin Lochore. "Dermato-informatic approaches to understanding and improving lesional diagnostic expertise in cutaneous oncology." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/31068.
Full textAbstract:
Cutaneous malignancies represent a quarter of all new cancer diagnoses in the UK. The key to reducing the tumours’ associated mortality and morbidity is early diagnosis and treatment. Prompt diagnosis remains predominately a clinical skill, but relatively little investigation of the cognitive psychology underpinning expertise in this domain has been undertaken. This thesis aims to improve understanding of these processes and investigate how lesional diagnostic expertise might be enhanced. A large database of diagnostically tagged images was captured specifically for this project. A series of separate studies were undertaken to give insight into how lesional diagnosis occurs and how it can be improved. The studies highlighted that non-analytical pattern recognition (NAPR) is likely to predominate in distinguishing malignant and non-malignant skin lesions and that the widely-promoted rules advocating analytical pattern recognition (APR) are not effective for discriminating melanoma from benign pigmented lesions. The keystone to promoting the development of NAPR and thus diagnostic expertise would seem to be increasing a novice’s personal library of examples with relevant feedback. Studies demonstrated that current undergraduate exposure was variable but universally sparse, so simulation by way of diagnostically tagged images was developed which showed accuracy could be improved by increased exposure. This improvement occurred in both a content specific and dose responsive manner. These studies also highlighted that the learning curves for skin lesions are not uniform. Further studies demonstrated that the choice of images had implications on the development of diagnostic expertise; suggesting it was important that these images represent clinical practice rather than “classic” examples traditionally advocated for teaching purposes. In addition, studies highlighted the potential benefit of the 3D models developed during this project. Building on the idea that a personal catalogue of relevant referent images was crucial to enhanced diagnostic accuracy, prototype software was developed to exteriorise the experts’ library of examples; in the tests described novices utilising the software delivered superior accuracy than medical students on the completion of their undergraduate teaching. In summation, the work described shows that by utilising dermato-informatic approaches lesional diagnostic competence can be improved significantly.
21
Korotkov, Konstantin. "Automatic change detection in multiple pigmented skin lesions." Doctoral thesis, Universitat de Girona, 2014. http://hdl.handle.net/10803/260162.
Full textAbstract:
Malignant melanoma is the rarest and deadliest of skin cancers causing three times more deaths than all other skin-related malignancies combined. Fortunately, in its early stages, it is completely curable, making a total body skin examination (TBSE) a fundamental procedure for many patients. Despite the advances in body scanning techniques, automated assistance tools for TBSEs have not received due attention. This fact is emphasized in our literature review covering the area of computerized analysis of PSL images. Aiming at the automation of TBSEs, we have designed and built a total body scanner to acquire skin surface images using cross-polarized light. Furthermore, we have developed an algorithm for the automated mapping of PSLs and their change estimation between explorations. The initial tests of the scanner showed that it can be successfully applied for automated mapping and temporal monitoring of multiple lesionsEl melanoma maligne és el més rar i mortal de tots els càncers de pell, causant tres vegades més morts que el conjunt de totes les altres malalties malignes de la pell. Afortunadament, en les primeres etapes, és completament curable, fent de les exploracions de pell a nivell de cos complert (TBSE en anglès) un procés fonamental per a molts pacients. Malgrat els avenços en les tècniques d’escaneig cutani, les eines per a realitzar TBSEs de forma automàtica no han rebut massa atenció. Per tant, hem dissenyat i construït un escàner corporal de cobertura total per adquirir imatges de la superfície de la pell utilitzant llum amb polarització creuada. A més, hem desenvolupat un algoritme pel mapeig automàtic de les PSLs i l’estimació dels canvis entre exploracions. Els tests inicials de l’escàner mostren que aquest pot ésser utilitzat satisfactòriament pel mapeig automàtic i el control de canvis temporal de múltiples lesions
22
Dhinagar, Nikhil J. "Morphological Change Monitoring of Skin Lesions for Early Melanoma Detection." Ohio University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1533911373953079.
Full text
23
Wright, Lindsay. "The health beliefs and behaviour of older people in relation to the early detection of skin cancer." Thesis, University of Central Lancashire, 1999. http://clok.uclan.ac.uk/20274/.
Full textAbstract:
There is an apparent imbalance in the psychological literature towards research with young people. As the proportion of older people in the population increases, and as people live longer, this imbalance will have to be redressed. This thesis consists of three studies which were carried out to look at the early detection of skin cancer in older people. In Study I semistructured interviews were carried out with ten men and ten women aged 55 to 87 years. Qualitative analysis revealed a number of misconceptions, and a lack of awareness of the increased risk associated with increasing age. In Study II 265 men and women aged 55 to 90 years completed structured questionnaires based on an expanded health belief model (FIBM) which incorporated specific fmdings from Study I. Results show that the expanded FIBM explained a reasonable amount of variance in skin self-examination (SSE), but a negligible amount in treatment-seeking intentions. Path analysis showed that the HBM functioned as a model to some degree, but that the variables were actually working more or less independently rather than together. The future of the HBM is discussed in terms of alternative ways in which it can be tested, and considerations which need to be taken when applying it to particular samples, such as older people. In Study ifi a sample of forty-four GPs were presented with selected items from Study II, and asked to estimate how the sample of older people had responded. The results suggested that the GPs were not sensitive to the beliefs and behaviour of the older people. However, closer inspection of the results showed a strong response bias which limited the degree to which any conclusions could be drawn from the study. Overall, the research has shown that age-related factors need to be considered when looking at preventive health behaviour. The need to take a broader, less individualistic, approach to health behaviour is also discussed, in terms of both health promotion practice and the development of theoretical models of behaviour.
24
Williams, Gerald R. "An Analysis of Spectral Selectivity on Edge Detection Algorithms for a Non-invasive Identification of Skin Cancer." NSUWorks, 2007. http://nsuworks.nova.edu/gscis_etd/927.
Full textAbstract:
Skin cancer is the number one form of cancer in humans today. Most moles or lesions are non-cancerous (defined as benign); however, a small percentage may actually be cancerous and can ultimately be fatal. A doctor has to draw from his or her own experience and manually inspect lesions for characteristics of skin cancer during a patient's medical exam to determine whether a lesion is cancerous or benign. Being able to make this distinction could prevent serious life-threatening conditions from going undiagnosed. A review of existing literature finds that most of the current research is now focused on using computer imagery to assist the doctor in this evaluation. The basic steps include capturing the image, defining the image (e.g., shape), performing any enhancements necessary to the image (e.g., hair removal), and finally analyzing and storing the characteristics of that image (e.g., asymmetry, border, dimensions, etc.). The image has a shape that will need to be defined and this is accomplished through a process known as edge detection. Edge detection algorithms identify and locate discontinuities in the pixel intensities of an image. The discontinuities are typically associated with abrupt changes in pixel intensity values that characterize the boundaries of the objects. Since current research depends so much on computer imagery and accurate edge detection, new research should focus on what can be done to enhance the image for edge detection analysis. This dissertation focused on analyzing images of skin lesions with filtered light to determine if there were visible or non-visible characteristics of potentially cancerous lesions that the human eye could not see. This was accomplished by measuring the affect that different wavelength filters (spectral selectivity) had on lesion parameters. While this was only a small piece of the problem, it was an important building block necessary for an automated visual inspection system. Serious life threatening conditions could be better diagnosed if there was a certified visual inspection system that could distinguish benign lesions from cancerous lesions. This dissertation along with the existing body of research established a foundation on which a future system could be designed and deployed that would aid patients as well as doctors by providing additional information that can result in an improved diagnosis.
25
Fleshman, Shane Killian. "IMPEDANCE-BASED DETECTION OF TISSUE USING A MULTI-ELECTRODE DEVICE." DigitalCommons@CalPoly, 2011. https://digitalcommons.calpoly.edu/theses/643.
Full textAbstract:
Melanoma skin cancer is the abnormal growth of the melanocytes – the pigmented cells located in the epidermis. The current gold standard diagnostic technique for determining whether a lesion is cancerous involves subjectively examining suspicious lesions and performing an invasive biopsy to confirm melanoma. This method may neglect some lesions or cause scarring from biopsies that turn out to be benign. Thus, impedance-based detection using a multi-electrode device was investigated as a noninvasive technique to diagnose melanoma skin cancer. The multi-electrode device was designed with 8 equally spaced Ag/AgCl electrodes surrounding one central electrode at a 5 mm radius. The electrodes were held in place by a vice-like mechanism using three circular Delrin sections. The electrodes were interfaced to an 8:1 multiplexer and National Instruments Educational Laboratory Virtual Instrumentation Suite (ELVIS) for measurement control and impedance analysis. The ELVIS system, multiplexer, and electrode device were validated for accuracy with various values of resistors and capacitors. Raw and cooked chicken thigh meat and skin were tested to evaluate the capabilities of the electrode device to discern different tissue types and tissue moisture contents by impedance measurements. EpidermTM and Melanoma tissue-engineered skin analogues, provided by MatTek Corporation, were tested to mimic the in situ disease state. The electrode device was found to produce reliable measurements for known electrical components with resistances between 10 ohms and 100 k-ohms and capacitances between 10 nF and 10 uF. The measurements from the chicken tissues and tissue-engineered skin constructs – excluding cooked chicken skin data – fell within the reliable range of the electrode device and were thus considered reliable as well. All analyses concluded that a statistical difference between the impedances of raw meat and raw skin, cooked meat and raw meat, and EpidermTM and Melanoma existed. Therefore, the hypothesis that a multi-electrode device could differentiate between melanoma and healthy skin tissues based on impedance measurements was satisfied.
26
Chait, Sari R. "Body Image, Attitudes, and Self-Efficacy as Predictors of Past Behavior and Future Intention to Perform Breast and Skin Self-Examinations." Scholar Commons, 2006. https://scholarcommons.usf.edu/etd/664.
Full textAbstract:
Breast self-examinations (BSE) and skin self-examinations (SSE) provide people with a cost-effective and time-efficient approach to the detection of cancer. Given the utility of these self-examination behaviors, it is important to determine who is likely to perform them regularly and why. Prior research has demonstrated a relationship of attitudes and self-efficacy with performance of and intention to practice the exams. Body image, although not previously studied, can be hypothesized to influence performance of these behaviors as well. To address these issues, the current study sought to determine if there are relationships between attitudes, self-efficacy, and body image and the practice of BSE and SSE. One hundred and six women completed measures assessing attitudes, self-efficacy, body image, past performance of BSE and SSE, and future intention to perform BSE and SSE. Results indicated that attitude was positively related to past performance of BSE and SSE and future intention to perform SSE. Self-efficacy was positively related to past performance and future intention to perform BSE and SSE. Body image was positively related only to past performance of SSE. Future research should further examine the relationship between body image and SSE utilizing longitudinal designs and a more diverse population.
27
Zanville, Noah Robert. "Evaluating Local Skin Heating as an Early Detection Method for Small-fiber Neuropathy in Women with Breast Cancer Receiving Paclitaxel (Taxol(RTM))." Thesis, Indiana University - Purdue University Indianapolis, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10839712.
Full textAbstract:
The purpose of this prospective, observational study was to determine if a technique used to detect early signs of small-fiber neuropathy (local skin heating) could detect signs of small-fiber taxane-induced peripheral neuropathy (TIPN) in breast cancer survivors (BCS) during the first 6 weeks of Taxol®. Aims of the study were to compare the mean size of (1) axon reflexes and (2) axon flares (both markers of small-fiber nerve function) in BCS receiving Taxol® to the size of reflexes/flares in healthy female controls (HCs). A third aim was to determine whether the size of axon reflexes/flares correlated with (a) overall TIPN severity and (b) severity of individual signs/symptoms of TIPN during early Taxol ®.
Data for the study was collected from nine BCS and 20 HCs (N = 29). All BCS had first-time, non-metastatic cancer and received weekly or bi-weekly Taxol®. Data was collected at 3 time-points: Time 1 (day 0, before Taxol®), Time 2 (day 14), and Time 3 (day 42). Axon reflexes and flares were generated using a validated 40-minute skin heating protocol. Axon reflexes were measured using laser Doppler Flowmetry. Axon flares were measured using full-field laser perfusion imaging. TIPN was measured using the 5-item Short Form of the Total Neuropathy Score (Reduced Version).
Results identified potential signs of small-fiber TIPN in BCS after 6 weeks of Taxol®. Contrary to expectation, axon reflexes were larger for BCS at Time 3 than HCs, suggesting that Taxol® may be associated with an increase in small-fiber nerve function like that seen in pre-clinical studies. Clinical signs/symptoms of TIPN were not significantly correlated with axon reflexes or axon flares at the same time point. Analyses of axon flare size were confounded by issues with the data.
These results add to the growing body of evidence showing that Taxol ® affects small-diameter sensory nerves and provides the first evidence in humans that changes in small-fiber nerve function may be detectable after just 6 weeks of Taxol® therapy. Studies in larger samples are needed to validate these findings.
28
Mansutti, Giulia. "Analysis and design of innovative antenna systems for telecommunications and health applications." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3421863.
Full textAbstract:
In this thesis the study of different innovative antenna systems is presented. The antenna designs that have been analyzed and that are described in this thesis can be divided in three main groups based on their application: phased-array antennas working on surfaces that change shape in time, millimeter-wave antennas for skin cancer diagnosis, and gaseous plasma antennas for satellite communications.
As far as the first topic is concerned, the research activity presented in this thesis focuses on the study of the strengths and limitations of a specific pattern recovery technique: the projection method. This technique has been adopted to retrieve the radiation properties of linear and planar arrays placed on surfaces whose shape changes in time according to different geometrical deformation; moreover, its effectiveness was assessed for both broadside and beam steering arrays, leading to a novel and simpler formulation of this pattern recovery technique for arrays whose beam is tilted towards different directions. The results have been obtained both through full-wave numerical simulations in CST Microwave Studio and through measurements performed in collaboration with the North Dakota State University (NDSU), Fargo, North Dakota, USA.
Regarding skin cancer diagnosis, a novel substrate integrated waveguide probe for early-stage skin cancer detection has been designed as well: this probe is cheap and easy to fabricate and can achieve high accuracy in detecting small early-stage skin cancer, thus providing a tool with the potential of being adopted as a real aid for skin cancer diagnosis. The probe has been tested both through full-wave numerical simulations and through measurements on a skin phantom realized at The University of Queensland with the support of the Microwave Team.
The study about gaseous plasma antennas for satellite communications has been mainly numerical. Different designs have been conceived with the aim of exploiting the advantages of using plasma elements while at the same time avoiding the limitations related to this novel technology. A practical implementation of these designs is now the subject of a collaboration with CISAS B. Colombo, an aerospace research center of the University of Padova.
29
Noori, Hoshyar A. "Automatic skin cancer detection system." Thesis, 2014. http://hdl.handle.net/10453/37020.
Full textAbstract:
University of Technology Sydney. Faculty of Engineering and Information Technology.During recent decades, the incident of malignant melanoma as the lethal form of skin cancer has been raised. The occurrence in Australia is much higher than US, UK, and Canada with the cases more than 10,000 diagnosis and annual mortality of 1250 people. The persistent raise of this cancer in the worldwide, the high medical cost and death rate have prioritized the early diagnosis of this cancer. The anticipation and cure of melanoma is strictly relevant to its thickness, if it can be detected early, the survival rate would be increased. Although lots of effort has been made to advance the detection of skin cancers, the challenging concerns still about it. The computer-based detection systems can improve the diagnosis rate of melanoma by 5–30% in comparison with the naked-eye. Since the visual perception often involve some faults, the necessity of second opinion with higher accuracy and reliability is highlighted. On the other hand, it reduces the task and responsibilities that are performed by physicians. Many researches have been developed in automated detection of melanoma. The potential advantages of such studies are significant and incalculable. Moreover, the difficulties entangle are a lot, and the new contributions in the area are highly appreciated. However, it is extensively acknowledged that the more trustful and reliable detection systems require higher accuracy. The purpose of this thesis is to propose an algorithm for skin cancer diagnosis that is able to classify lesions as malignant or benign automatically. The different components in an automated diagnosis of skin cancer includes: Pre-processing, segmentation, feature extraction and selection, and classification. In this thesis, after selecting the best image enhancement techniques which are achieved by applying and comparing different noise removal and contrast enhancement techniques on images, the segmentation stage is performed. In this stage, a fully automated segmentation algorithm in dermoscopy images based on k-mean and level-set algorithms are proposed and compared with other algorithms mentioned in this thesis using statistical tools. Proposed algorithm shows the improvement in the results. In the next stage, after extracting the various features of images, a fully automated feature selection algorithm, Smart PSO-SVM, which optimizes the feature selection stage, is proposed. Comparative study of proposed algorithm with other algorithms is performed to analyse the performance of proposed algorithm among others. The results obtained in the best subset of features which feed the classification stage. In classification stage, the use of SA-SVM as a new classifier in the area of skin cancer detection systems is proposed. The average accuracy and F-score are estimated as 87.0611% and 0.9167 respectively. The statistical evaluation using t-test also shows the superiority of proposed algorithm when compares with other algorithms in this thesis.
30
Maurer, Ann-Kathrin. "Skingenio: using artificial intelligence for early detection of skin cancer." Master's thesis, 2020. http://hdl.handle.net/10362/104087.
Full textAbstract:
Startup companies stemming from novel scientific breakthroughs have had an enormous impact on the generation of new, disruptive products over the last fifty years.This thesis presents the vision for a new company,SkingenioGmbH, launched with the aim of commercializing an artificial intelligenceable to identify the malignancy of skin lesions using only photographs. The new company thus addresses theimportant market of skin cancer prevention.The author’s visionis presented inthe form of aforward-looking business and implementation plan that covers all key aspects an entrepreneur has to take into account when building a new, science-based venture.
31
Zanville, Noah Robert. "Evaluating local skin heating as an early detection method for small-fiber neuropathy in women with breast cancer receiving paclitaxel (Taxol®)." Diss., 2018. https://doi.org/10.7912/C2QH1H.
Full textAbstract:
Indiana University-Purdue University Indianapolis (IUPUI)The purpose of this prospective, observational study was to determine if a technique used to detect early signs of small-fiber neuropathy (local skin heating) could detect signs of small-fiber taxane-induced peripheral neuropathy (TIPN) in breast cancer survivors (BCS) during the first 6 weeks of Taxol®. Aims of the study were to compare the mean size of (1) axon reflexes and (2) axon flares (both markers of small fiber nerve function) in BCS receiving Taxol® to the size of reflexes/flares in healthy female controls (HCs). A third aim was to determine whether the size of axon reflexes/flares correlated with (a) overall TIPN severity and (b) severity of individual signs/symptoms of TIPN during early Taxol®. Data for the study was collected from nine BCS and 20 HCs (N = 29). All BCS had first-time, non-metastatic cancer and received weekly or bi-weekly Taxol®. Data was collected at 3 time-points: Time 1 (day 0, before Taxol®), Time 2 (day 14), and Time 3 (day 42). Axon reflexes and flares were generated using a validated 40-minute skin heating protocol. Axon reflexes were measured using laser Doppler Flowmetry. Axon flares were measured using full-field laser perfusion imaging. TIPN was measured using the 5-item Short Form of the Total Neuropathy Score (Reduced Version). Results identified potential signs of small-fiber TIPN in BCS after 6 weeks of Taxol®. Contrary to expectation, axon reflexes were larger for BCS at Time 3 than HCs, suggesting that Taxol® may be associated with an increase in small-fiber nerve function like that seen in pre-clinical studies. Clinical signs/symptoms of TIPN were not significantly correlated with axon reflexes or axon flares at the same time point. Analyses of axon flare size were confounded by issues with the data. These results add to the growing body of evidence showing that Taxol® affects small-diameter sensory nerves and provides the first evidence in humans that changes in small-fiber nerve function may be detectable after just 6 weeks of Taxol® therapy. Studies in larger samples are needed to validate these findings.
32
Chen, Tzu-Yin, and 陳姿尹. "Measurements of Skin Dose in Head and Neck Cancer Radiotherapy by MOSFET Detector." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/17528130330691577413.
Full textAbstract:
碩士中臺科技大學
醫學影像暨放射科學系暨研究所
101
Many tumors of head and neck cancer patients are located near the surface area in radiation therapy. In order to compensate the underdose due to skin sparing effect, the treatment planning system will apply the higher fluence to satisfy the dose constrains of the target, and therefore cause the obvious skin reaction after radiation therapy. This work used two types of Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET), TN-502RD and TN-502RDM, measuring the dose linearity, energy dependence, angular dependence, reproducibility and dose rate dependence, then the skin dose in head and neck cancer patients using linac and tomotherapy. The comparison of calculation dose versus measurement were also evaluated . The results showed the maximum variation of 36 % only in angular dependence for both types detector. There were not significant dependence on the other characteristic measurements of MOSFET. We found TN-502RD had more obvious angular dependence than TN-502RDM. In MOSFET measurements and treatment planning system calculation values comparison of skin dose for head and neck cancer clinical applications, the discrepancy were -0.9 % to 5 % and 2.1 % to 46.1 % for tomotherapy and linac , respectively.