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Use of Deep Learning in Detection of Skin Cancer and Prevention of Melanoma
KTH, School of Technology and Health (STH).
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

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.

Place, publisher, year, edition, pages
2017. , 38 p.
Series
TRITA-STH, 2017:74
Keyword [en]
Clinical images, Deep Learning, Dermoscopical images, Inception v3, Learning rate, Melanoma, Transfer Learning, VGG-16
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:kth:diva-209007OAI: oai:DiVA.org:kth-209007DiVA: diva2:1109613
External cooperation
Teleskin, Belgrade, Serbia
Subject / course
Medical Engineering
Educational program
Master of Science - Medical Engineering
Presentation
2017-06-08, T53, HÄLSOVÄGEN 11 C, Stockholm, 14:00 (English)
Supervisors
Examiners
Available from: 2017-06-15 Created: 2017-06-14 Last updated: 2017-06-15Bibliographically approved

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CiteExportLink to record
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