Melanoma, a severe and life-threatening skin cancer, is commonly misdiagnosed
or left undiagnosed. Advances in artificial intelligence, particularly deep learning,
have enabled the design and implementation of intelligent solutions to skin lesion
detection and classification from visible light images, which are capable of performing
early and accurate diagnosis of melanoma and other types of skin diseases. This work
presents solutions to the problems of skin lesion segmentation and classification. The
proposed classification approach leverages convolutional neural networks and transfer
learning. Additionally, the impact of segmentation (i.e., isolating the lesion from the
rest of the image) on the performance of the classifier is investigated, leading to the
conclusion that there is an optimal region between “dermatologist segmented” and
“not segmented” that produces best results, suggesting that the context around a
lesion is helpful as the model is trained and built. Generative adversarial networks,
in the context of extending limited datasets by creating synthetic samples of skin
lesions, are also explored. The robustness and security of skin lesion classifiers using
convolutional neural networks are examined and stress-tested by implementing
adversarial examples. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection
| Identifer | oai:union.ndltd.org:fau.edu/oai:fau.digital.flvc.org:fau_40784 |
| Contributors | Burdick, John B. (author), Marques, Oge (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science |
| Publisher | Florida Atlantic University |
| Source Sets | Florida Atlantic University |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation, Text |
| Format | 165 p., application/pdf |
| Rights | Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder., http://rightsstatements.org/vocab/InC/1.0/ |
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