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Tesi sul tema "Dermoscopic Images"

Autore: Grafiati

Pubblicato: 4 giugno 2025

Ultima modifica: 1 agosto 2025

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1

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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2

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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3

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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4

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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5

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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6

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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Abstract (sommario):

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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7

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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8

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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Abstract (sommario):

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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9

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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Abstract (sommario):

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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10

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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Abstract (sommario):

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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11

Ferreira, Pedro. "Contributions to the segmentation of dermoscopic images." Dissertação, 2012. http://hdl.handle.net/10216/73322.

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12

Ferreira, Pedro Miguel Martins. "Contributions to the segmentation of dermoscopic images." Master's thesis, 2012. http://hdl.handle.net/10216/68423.

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13

Ferreira, Pedro Miguel Martins. "Contributions to the segmentation of dermoscopic images." Master's thesis, 2012. https://repositorio-aberto.up.pt/handle/10216/64925.

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14

Ferreira, Pedro Miguel Martins. "Contributions to the segmentation of dermoscopic images." Dissertação, 2012. http://hdl.handle.net/10216/68423.

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15

Góis, Luís Manuel Gonçalves. "Automatic classification of cutaneous lesions using dermoscopic images." Master's thesis, 2017. http://hdl.handle.net/10316/82958.

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Abstract (sommario):

Trabalho de Projeto do Mestrado Integrado em Engenharia Biomédica apresentado à Faculdade de Ciências e Tecnologia<br>Segundo dados estatísticos o melanoma maligno é a forma mais mortífera de cancro da pele, com uma taxa de incidência crescente ao longo das últimas décadas, em todo o mundo. No entanto, é a mais tratável, dependendo do estado do cancro, já que pesquisas mais aprofundadas mostraram que a deteção prematura e o tratamento do melanoma significam maior probabilidade de cura. No entanto, o diagnóstico clinico do melanoma é uma tarefa desafiante para os dermatologistas, visto que os p

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16

Ferreira, Tadeu Amora de Souza. "Comparative Analysis and Assessment Evolution of Dermascopic Images." Master's thesis, 2018. http://hdl.handle.net/10316/86226.

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Trabalho de Projeto do Mestrado Integrado em Engenharia Biomédica apresentado à Faculdade de Ciências e Tecnologia<br>De acordo com as estatísticas, o cancro da pele é um dos cancros mais prevalentes mundialmente com um rápido aumento do número de casos nas últimas décadas. O Melanoma é o cancro da pele mais perigoso, causando um maior número de mortes que qualquer outro cancro. No entanto, através de uma monitorização regular o seu diagnóstico prematuro e consequente irradicação é possível. Através da monitorização da pele, os dermatologistas são capazes de localizar alterações de padrões em

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17

黃睦庭. "A Study on Dermoscopy Images Feature Recognition for Basal Cell Carcinoma." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/93213628088371486513.

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碩士<br>逢甲大學<br>生醫資訊暨生醫工程碩士學程<br>103<br>Abstract Basal Cell Carcinoma (BCC) is one of skin cancers which occur very often in human. Although BCC is the most common skin cancer in Caucasians, many Asian patients also suffer from this skin disease. The incidence rate of BCC is increasing steadily with the risk of becoming a deadly disease. However, rare researches in Asia focus on computer-aided analysis of the disease. The purpose of this BCC study is to classify skin lesions as malignant or benign by computer-aided diagnosis. We propose a method to discriminate BCC from skin lesions of dermoscop

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18

Silva, Cátia Sofia Pinho. "Feature extraction for automatic classification of cutaneous lesions from dermoscopy images." Master's thesis, 2012. https://repositorio-aberto.up.pt/handle/10216/74356.

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19

Silva, Cátia Sofia Pinho. "Feature extraction for automatic classification of cutaneous lesions from dermoscopy images." Dissertação, 2012. https://repositorio-aberto.up.pt/handle/10216/74356.

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20

Lé, Ricardo Pereira Cunha de Sousa. "Automatic analysis of skin lesions in dermoscopy images: feature extraction and classification." Master's thesis, 2015. https://repositorio-aberto.up.pt/handle/10216/80395.

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21

Lé, Ricardo Pereira Cunha de Sousa. "Automatic analysis of skin lesions in dermoscopy images: feature extraction and classification." Dissertação, 2015. https://repositorio-aberto.up.pt/handle/10216/80395.

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22

Veiga, Joana Raquel Martins. "Analysis of Temporal Variations in Dermoscopy Images of Pigmented Skin Lesions by Machine Learning Techniques." Master's thesis, 2018. https://repositorio-aberto.up.pt/handle/10216/114122.

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Abstract (sommario):

Each year more people are diagnosed with skin cancer all over the world. The large incidence in populations is causing a huge concern to the scientific community, which leads the development of multiple studies related to diagnose this type of cancer.Therefore computer-aided systems are becoming more important in this field due to the challenging task of discriminate benign from malignant skin lesions. These systems can process several images and are intended to make a decision based on the diagnosis achieved by the processing of the images which will reduce the dependency on the experience of

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23

Veiga, Joana Raquel Martins. "Analysis of Temporal Variations in Dermoscopy Images of Pigmented Skin Lesions by Machine Learning Techniques." Dissertação, 2018. https://repositorio-aberto.up.pt/handle/10216/114122.

Testo completo

Abstract (sommario):

Each year more people are diagnosed with skin cancer all over the world. The large incidence in populations is causing a huge concern to the scientific community, which leads the development of multiple studies related to diagnose this type of cancer.Therefore computer-aided systems are becoming more important in this field due to the challenging task of discriminate benign from malignant skin lesions. These systems can process several images and are intended to make a decision based on the diagnosis achieved by the processing of the images which will reduce the dependency on the experience of

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