Skin cancer is a major medical problem. If not detected early enough, skin cancer like
melanoma can turn fatal. As a result, early detection of skin cancer, like other types of
cancer, is key for survival. In recent times, deep learning methods have been explored to
create improved skin lesion diagnosis tools. In some cases, the accuracy of these methods
has reached dermatologist level of accuracy. For this thesis, a full-fledged cloud-based
diagnosis system powered by convolutional neural networks (CNNs) with near
dermatologist level accuracy has been designed and implemented in part to increase early
detection of skin cancer. A large range of client devices can connect to the system to
upload digital lesion images and request diagnosis results from the diagnosis pipeline.
The diagnosis is handled by a two-stage CNN pipeline hosted on a server where a
preliminary CNN performs quality check on user requests, and a diagnosis CNN that
outputs lesion predictions. / 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_40900 |
| Contributors | Akar, Esad (author), Furht, Borko (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 | 55 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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