Relevant bibliographies by topics / Dermoscopic Images

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Dermoscopic Images'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Dermoscopic Images"

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Yilmaz, Abdurrahim, Gulsum Gencoglan, Rahmetullah Varol, Ali Anil Demircali, Meysam Keshavarz, and Huseyin Uvet. "MobileSkin: Classification of Skin Lesion Images Acquired Using Mobile Phone-Attached Hand-Held Dermoscopes." Journal of Clinical Medicine 11, no. 17 (2022): 5102. http://dx.doi.org/10.3390/jcm11175102.

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Dermoscopy is the visual examination of the skin under a polarized or non-polarized light source. By using dermoscopic equipment, many lesion patterns that are invisible under visible light can be clearly distinguished. Thus, more accurate decisions can be made regarding the treatment of skin lesions. The use of images collected from a dermoscope has both increased the performance of human examiners and allowed the development of deep learning models. The availability of large-scale dermoscopic datasets has allowed the development of deep learning models that can classify skin lesions with hig
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Foltz, Emilie A., Joanna Ludzik, Sancy Leachman, et al. "Revolutionizing Skin Cancer Triage: The Role of Patient-Initiated Teledermoscopy in Remote Diagnosis." Cancers 16, no. 14 (2024): 2565. http://dx.doi.org/10.3390/cancers16142565.

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Introduction: Teledermatology, defined as the use of remote imaging technologies to provide dermatologic healthcare services to individuals in a distant setting, has grown considerably in popularity since its widespread implementation during the COVID-19 pandemic. Teledermoscopy employs a smartphone dermatoscope attachment paired with a smartphone camera to visualize colors and microstructures within the epidermis and superficial dermis that cannot be seen with the naked eye ABCD criteria alone. Methods: Our retrospective observational cohort and case–control study evaluated the utility of loa
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Chuh, Antonio, Vijay Zawar, Regina Fölster-Holst, Gabriel Sciallis, and Thomas Rosemann. "Twenty-five practical recommendations in primary care dermoscopy." Journal of Primary Health Care 12, no. 1 (2020): 10. http://dx.doi.org/10.1071/hc19057.

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ABSTRACT Dermoscopy in primary care enhances clinical diagnoses and allows for risk stratifications. We have compiled 25 recommendations from our experience of dermoscopy in a wide range of clinical settings. The aim of this study is to enhance the application of dermoscopy by primary care clinicians. For primary care physicians commencing dermoscopy, we recommend understanding the aims of dermoscopy, having adequate training, purchasing dermoscopes with polarised and unpolarised views, performing regular maintenance on the equipment, seeking consent, applying contact and close non-contact der
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Berglund, Sofia, John Paoli, Petra Svensson, Karin Terstappen, Martin Gillstedt, and Johan Dahlén Gyllencreutz. "Patient Ability to Take Dermoscopic Follow-Up Images of Atypical Melanocytic Lesions With Smartphones: A Pilot Study." Dermatology Practical & Conceptual 14, no. 4 (2024): e2024268. http://dx.doi.org/10.5826/dpc.1404a268.

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Introduction: Short-term teledermoscopic monitoring helps to distinguish early melanomas from nevi. As the incidence of melanoma is increasing, there are several benefits of patients taking their own dermoscopic images, but only a few previous studies have investigated the feasibility of this approach. Objectives: We sought to examine patient ability to take evaluable dermoscopic images of atypical melanocytic lesions in need of short-term monitoring. Methods: Patients were asked to take follow-up images in their homes using a borrowed dermoscope and their own smartphone. We investigated if th
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Alhasan, Jawidan Kamal, and Mohammad Yousif Saeed Jaf. "Characteristic dermoscopic signs of Melasma among patients attending dermatology-teaching centre in Sulaymaniyah city." Advanced medical journal 7, no. 1 (2022): 62–69. http://dx.doi.org/10.56056/amj.2022.160.

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Background and objectives: Melasma is a common acquired pigmentary disorder which mainly involves sun exposed areas and characterized by the presence of single or multiple hyperpigmented patches distributed symmetrically over face and extending up to neck. Dermoscopy is a noninvasive technique has been tapped recently in various hyperpigmentary conditions like melasma. This research aims to determine the characteristic dermoscopic signs of melasma. Methods: A descriptive study of 100 patients with melasma were conducted over a period of 4 months, these patients were diagnosed clinically and di
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Dodeja, Ajay, Sushil Pande, and Bhushan Madke. "Dermoscopy of oral and genital mucosal lesions: A descriptive cross-sectional study protocol." PLOS ONE 18, no. 8 (2023): e0289562. http://dx.doi.org/10.1371/journal.pone.0289562.

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Introduction Dermoscopy is a safe, rapid, and non-invasive tool that aids in the clinical examination of pigmented and non-pigmented lesions. The upward trend in the use of dermoscopy can be attributed to the availability of compact hand-held and sophisticated dermoscopes, that are small enough to be carried around in a pocket. The extent of dermoscopy is not only limited to the evaluation of cutaneous lesions but also involves its use in the assessment of mucosal lesions along with lesions of hair and nails. Methods In a descriptive cross-sectional study, subjects (n = 100) with oral or genit
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Polesie, Sam, and John Paoli. "Interobserver and Human–Artificial Intelligence Concordance in Differentiating Between Invasive and In Situ Melanoma." Iproceedings 8, no. 1 (2022): e36895. http://dx.doi.org/10.2196/36895.

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Background Machine learning algorithms including convolutional neural networks (CNNs) have recently made significant advances in research settings. Even though several algorithms nowadays are targeted directly to the consumer market, their implementation in clinical practice is still pending. Most melanomas are easy to recognize even without the aid of dermoscopy. Nonetheless, it is often more challenging to discriminate between invasive melanoma and melanoma in situ (MIS) in a preoperative setting even with the assistance of dermoscopy. Although several dermoscopic features suggestive of MIS
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Kentley, Jonathan, Jochen Weber, Konstantinos Liopyris, et al. "Agreement Between Experts and an Untrained Crowd for Identifying Dermoscopic Features Using a Gamified App: Reader Feasibility Study." JMIR Medical Informatics 11 (January 18, 2023): e38412. http://dx.doi.org/10.2196/38412.

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Background Dermoscopy is commonly used for the evaluation of pigmented lesions, but agreement between experts for identification of dermoscopic structures is known to be relatively poor. Expert labeling of medical data is a bottleneck in the development of machine learning (ML) tools, and crowdsourcing has been demonstrated as a cost- and time-efficient method for the annotation of medical images. Objective The aim of this study is to demonstrate that crowdsourcing can be used to label basic dermoscopic structures from images of pigmented lesions with similar reliability to a group of experts.
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Andrade, Catarina, Luís F. Teixeira, Maria João M. Vasconcelos, and Luís Rosado. "Data Augmentation Using Adversarial Image-to-Image Translation for the Segmentation of Mobile-Acquired Dermatological Images." Journal of Imaging 7, no. 1 (2020): 2. http://dx.doi.org/10.3390/jimaging7010002.

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Dermoscopic images allow the detailed examination of subsurface characteristics of the skin, which led to creating several substantial databases of diverse skin lesions. However, the dermoscope is not an easily accessible tool in some regions. A less expensive alternative could be acquiring medium resolution clinical macroscopic images of skin lesions. However, the limited volume of macroscopic images available, especially mobile-acquired, hinders developing a clinical mobile-based deep learning approach. In this work, we present a technique to efficiently utilize the sizable number of dermosc
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Polesie, Sam, and Oscar Zaar. "Frequency of Publication of Dermoscopic Images in Inter-observer Studies: A Systematic Review." Acta Dermato-Venereologica 101, no. 12 (2021): adv00621. http://dx.doi.org/10.2340/actadv.v101.865.

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Research interest in dermoscopy is increasing, but the complete dermoscopic image sets used in inter-observer studies of skin tumours are not often shared in research publications. The aim of this systematic review was to analyse what proportion of images depicting skin tumours are published in studies investigating inter-observer variations in the assessment of dermoscopic features and/or patterns. Embase, MEDLINE and Scopus databases were screened for eligible studies published from inception to 2 July 2020. For included studies the proportion of lesion images presented in the papers and/or
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Dissertations / Theses on the topic "Dermoscopic Images"

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Eltayef, Khalid Ahmad A. "Segmentation and lesion detection in dermoscopic images." Thesis, Brunel University, 2017. http://bura.brunel.ac.uk/handle/2438/16211.

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Malignant melanoma is one of the most fatal forms of skin cancer. It has also become increasingly common, especially among white-skinned people exposed to the sun. Early detection of melanoma is essential to raise survival rates, since its detection at an early stage can be helpful and curable. Working out the dermoscopic clinical features (pigment network and lesion borders) of melanoma is a vital step for dermatologists, who require an accurate method of reaching the correct clinical diagnosis, and ensure the right area receives the correct treatment. These structures are considered one of t
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Vatn, Niklas, and Julia Byström. "Evaluating automatic colour equalization to preprocess dermoscopic images for classification using a CNN." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302502.

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Skin cancer is one of the most prevalent types of cancer and diagnosing of skin lesions are mostly done by visual inspection by a doctor. Lately, computer- aided diagnosis (CAD) has gained popularity and previous studies have with great results utilized a convolutional neural network (CNN) to classify dermoscopic images of different benign and malignant skin lesions. While other studies using CAD tools have investigated the effects of using preprocessing methods on image data before using them in diagnosis classification. Therefore our thesis aims to investigate if preprocessing dermoscopic im
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Galda, Harald [Verfasser]. "Development of a segmentation method for dermoscopic images based on color clustering / Harald Galda." München : GRIN Verlag, 2004. http://d-nb.info/1179823397/34.

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Dong, Xu. "Segmenting Skin Lesion Attributes in Dermoscopic Images Using Deep Learing Algorithm for Melanoma Detection." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/86883.

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Melanoma is the most deadly form of skin cancer worldwide, which causes the 75% of deaths related to skin cancer. National Cancer Institute estimated that 91,270 new case and 9,320 deaths are expected in 2018 caused by melanoma. Early detection of melanoma is the key for the treatment. The image technique to diagnose skin cancer is dermoscopy, which leads to improved diagnose accuracy compared to traditional ABCD criteria. But reading and examining dermoscopic images is a time-consuming and complex process. Therefore, computerized analysis methods of dermoscopic images have been developed to
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Wazaefi, Yanal. "Automatic diagnosis of melanoma from dermoscopic images of melanocytic tumors : Analytical and comparative approaches." Thesis, Aix-Marseille, 2013. http://www.theses.fr/2013AIXM4106.

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Le mélanome est la forme la plus grave de cancer de la peau. Cette thèse a contribué au développement de deux approches différentes pour le diagnostic assisté par ordinateur du mélanome : approche analytique et approche comparative.L'approche analytique imite le comportement du dermatologue en détectant les caractéristiques de malignité sur la base de méthodes analytiques populaires dans une première étape, et en combinant ces caractéristiques dans une deuxième étape. Nous avons étudié l’impacte d’un système du diagnostic automatique utilisant des images dermoscopique de lésions cutanées pigme
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Nagane, Radhika. "Detection of flash in dermoscopy skin lesion images." Diss., Rolla, Mo. : University of Missouri-Rolla, 2007. http://scholarsmine.umr.edu/thesis/pdf/Nagane_09007dcc803ec3f9.pdf.

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Thesis (M.S.)--University of Missouri--Rolla, 2007.<br>Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed December 7, 2007) Includes bibliographical references (p. 89-90).
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Cheng, Beibei. "Automatic vessel and telangiectases analysis in dermoscopy skin lesion images." Diss., Rolla, Mo. : Missouri University of Science and Technology, 2009. http://scholarsmine.mst.edu/thesis/pdf/Cheng_09007dcc80636a0d.pdf.

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Thesis (M.S.)--Missouri University of Science and Technology, 2009.<br>Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed April 13, 2009) Includes bibliographical references (p. 46).
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Kaur, Ravneet. "THRESHOLDING METHODS FOR LESION SEGMENTATION OF BASAL CELL CARCINOMA IN DERMOSCOPY IMAGES." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/dissertations/1367.

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Purpose: Automatic border detection is the first and most crucial step for lesion segmentation and can be very challenging, due to several lesion characteristics. There are many melanoma border-detecting algorithms that perform poorly on dermoscopy images of basal cell carcinoma (BCC), which is the most common skin cancer. One of the reasons for poor lesion detection performance is that there are very few algorithms that detect BCC borders, because they are difficult to segment, even for dermatologists. This difficulty is due to low contrast, variation in lesion color and artifacts inside/outs
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Nie, Yali. "Automatic Melanoma Diagnosis in Dermoscopic Imaging Base on Deep Learning System." Licentiate thesis, Mittuniversitetet, Institutionen för elektronikkonstruktion, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-41751.

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Melanoma is one of the deadliest forms of cancer. Unfortunately, its incidence rates have been increasing all over the world. One of the techniques used by dermatologists to diagnose melanomas is an imaging modality called dermoscopy. The skin lesion is inspected using a magnification device and a light source. This technique makes it possible for the dermatologist to observe subcutaneous structures that would be invisible otherwise. However, the use of dermoscopy is not straightforward, requiring years of practice. Moreover, the diagnosis is many times subjective and challenging to reproduce.
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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.

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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
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Books on the topic "Dermoscopic Images"

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Galda, Harald. Development of a Segmentation Method for Dermoscopic Images Based on Color Clustering. GRIN Verlag GmbH, 2007.

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Celebi, M. Emre, Teresa Mendonca, and Jorge S. Marques. Dermoscopy Image Analysis. Taylor & Francis Group, 2015.

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Celebi, M. Emre, Teresa Mendonca, and Jorge S. Marques, eds. Dermoscopy Image Analysis. CRC Press, 2015. http://dx.doi.org/10.1201/b19107.

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Dermoscopy Image Analysis. Taylor & Francis Group, 2015.

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Celebi, M. Emre, Teresa Mendonca, and Jorge S. Marques. Dermoscopy Image Analysis. Taylor & Francis Group, 2015.

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Celebi, M. Emre, Teresa Mendonca, and Jorge S. Marques. Dermoscopy Image Analysis. Taylor & Francis Group, 2017.

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Surface Imaging for Biomedical Applications. Taylor & Francis Group, 2014.

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Hani, Ahmad Fadzil Mohamad. Surface Imaging for Biomedical Applications. Taylor & Francis Group, 2017.

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Hani, Ahmad Fadzil Mohamad. Surface Imaging for Biomedical Applications. Taylor & Francis Group, 2014.

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Hani, Ahmad Fadzil Mohamad. Surface Imaging for Biomedical Applications. Taylor & Francis Group, 2014.

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Book chapters on the topic "Dermoscopic Images"

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Sáez, Aurora, Begoña Acha, and Carmen Serrano. "Pattern Analysis in Dermoscopic Images." In Series in BioEngineering. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39608-3_2.

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Kozaczko, Kacper, Radoslaw Szpot, Andrzej Brodzicki, and Anna Wójcicka. "Automatic Artifact Removal from Dermoscopic Images." In Advanced, Contemporary Control. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35173-0_19.

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Liu, Jie, and Xian-biao Zou. "Computer-Aided Diagnosis of Dermoscopic Images." In Practical Dermoscopy. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1460-7_15.

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Arroyo, Jose Luis García, and Begoña García Zapirain. "Automated Detection of Melanoma in Dermoscopic Images." In Series in BioEngineering. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39608-3_6.

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Borys, Damian, Paulina Kowalska, Mariusz Frackiewicz, and Ziemowit Ostrowski. "A Simple Hair Removal Algorithm from Dermoscopic Images." In Bioinformatics and Biomedical Engineering. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16483-0_27.

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Safi, Asad, Maximilian Baust, Olivier Pauly, et al. "Computer–Aided Diagnosis of Pigmented Skin Dermoscopic Images." In Medical Content-Based Retrieval for Clinical Decision Support. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28460-1_10.

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Silva, Cátia S. P., André R. S. Marcal, Marta A. Pereira, Teresa Mendonça, and Jorge Rozeira. "Separability Analysis of Color Classes on Dermoscopic Images." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31298-4_32.

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Bethanney Janney. J, S. Emalda Roslin, and J. Premkumar. "Automatic Melanoma Diagnosis and Classification on Dermoscopic Images." In Health Information Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68723-6_14.

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Gulati, Savy, and Rosepreet Kaur Bhogal. "Classification of Melanoma from Dermoscopic Images Using Machine Learning." In Smart Intelligent Computing and Applications. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9282-5_32.

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Budhwant, Pranav, Sumeet Shinde, and Madhura Ingalhalikar. "Open-Set Recognition for Skin Lesions Using Dermoscopic Images." In Machine Learning in Medical Imaging. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59861-7_62.

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Conference papers on the topic "Dermoscopic Images"

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Madhavi, G. Bindu, Sumithra M, P. Yamini Devi, U. S. B. K. Mahalaxmi, T. Saravanan, and Pundru Chandra Shaker Reddy. "Skin Cancer Classification of Dermoscopic Images using Deep Learning." In 2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE). IEEE, 2024. https://doi.org/10.1109/aece62803.2024.10911365.

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Mejri, Sirine, and Afef Elloumi Oueslati. "Dermoscopic Images Classification Using Pretrained VGG-16 and ResNet-50 Models." In 2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP). IEEE, 2024. http://dx.doi.org/10.1109/atsip62566.2024.10638943.

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Melissa, Eleonora, Daria Riabitch, Linda Lazzeri, et al. "A Real-World Segmentation Model for Melanocytic and Nonmelanocytic Dermoscopic Images." In 12th International Conference on Bioimaging. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013129400003911.

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Kumar, Lalan, Kamred Udham Singh, Bhavesh Kumar Sharma, Manoj Singhal, Teekam Singh, and Linesh Raja. "Deep Learning and Crow Search to Detect Skin Cancer in Dermoscopic Images." In 2024 IEEE International Conference on Contemporary Computing and Communications (InC4). IEEE, 2024. http://dx.doi.org/10.1109/inc460750.2024.10649363.

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Maharajan, K., Harshitha S, I. Sri Vidya, Jajam Tejmai, and Gollapudi Katyayini. "Precision Diagnosis of Melanoma using Deep Learning Techniques on DICOM Dermoscopic Images." In 2024 3rd International Conference on Automation, Computing and Renewable Systems (ICACRS). IEEE, 2024. https://doi.org/10.1109/icacrs62842.2024.10841629.

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Elsawy, Radwa A., Ghada F. R. Hassan, Maram A. Wahba, Diaa-Eldin A. Mansour, and Amira S. Ashour. "Optimized End-to-End Coiflets Discrete Wavelet Transform for Dermoscopic Images Compression." In 2023 33rd International Conference on Computer Theory and Applications (ICCTA). IEEE, 2023. https://doi.org/10.1109/iccta60978.2023.10969395.

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Abuhammad, Huthaifa, Mohammad Alhawarat, and Dua'a Mehiar. "Soft Attention-Enhanced ResNet101 for Robust Skin Lesion Classification in Dermoscopic Images." In 2025 International Conference for Artificial Intelligence, Applications, Innovation and Ethics (AI2E). IEEE, 2025. https://doi.org/10.1109/ai2e64943.2025.10983896.

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Nasra, Parul. "Enhanced Melanoma Detection Using a Fine-Tuned EfficientNetV2-L Model on Dermoscopic Images." In 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI). IEEE, 2024. http://dx.doi.org/10.1109/icoici62503.2024.10696263.

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Talavera-Martinez, Lidia, Manuel Gonzalez-Hidalgo, and Pedro Bibiloni. "Hair removal in dermoscopic images using deep multitask learning." In LatinX in AI at Computer Vision and Pattern Recognition Conference 2022. Journal of LatinX in AI Research, 2022. http://dx.doi.org/10.52591/lxai202206245.

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When analyzing dermoscopic images, the hairs and their shadows on the skin may occlude relevant information about the lesion at the time of diagnosis. In this work, we present a new approach for hair removal on dermoscopic images based on deep learning techniques, as well as study in depth the behavior of the tasks of skin lesion segmentation, hair mask segmentation, and inpainting of those regions, in a multitasking framework to discover how tasks influence each other. Moreover, we describe our built database specifically for the task of hair removal. Qualitative and quantitative results demo
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Ozkan, Haydar. "Skin lesion border detection in dermoscopic images." In 2018 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT). IEEE, 2018. http://dx.doi.org/10.1109/ebbt.2018.8391431.

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Reports on the topic "Dermoscopic Images"

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Ho, Leyton. ully Automated GrowCut-based Segmentation of Melanoma in Dermoscopic Images. Journal of Young Investigators, 2019. http://dx.doi.org/10.22186/jyi.36.2.11-17.

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