Current Search: Akar, Esad (x)
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Title
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Cloud-based Skin Lesion Diagnosis System using Convolutional Neural Networks.
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Creator
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Akar, Esad, Furht, Borko, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
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Abstract/Description
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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...
Show moreSkin 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.
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Date Issued
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2018
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PURL
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http://purl.flvc.org/fau/fd/FA00013150
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Subject Headings
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Skin Diseases--diagnosis, Skin--Cancer--Diagnosis, Diagnosis--Methodology, Neural networks, Cloud computing
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Format
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Document (PDF)
