Dr. Nate George
Dr. Kevin Pyatt
Dr. Bob Mason
College of Computer and Information Sciences
MS Software Engineering
Thesis - Open Access
Number of Pages
Diagnosis of an unknown skin lesion is crucial to enable proper treatments. While curable with early diagnosis, only highly trained dermatologists are capable of accurately recognize melanoma skin lesions. Expert dermatologist classification for melanoma dermoscopic images is 65-66%. As expertise is in limited supply, systems that can automatically classify skin lesions as either benign or malignant melanoma are very useful as initial screening tools. Towards this goal, this study presents a convolutional neural network model, trained on features extracted from a highway convolutional neural network pretrained on dermoscopic images of skin lesions. This requires no lesion segmentation nor complex preprocessing. Further, it doesn’t cost much computational power to train the model. This proposed approach achieves a favorable training accuracy of 98%, validation accuracy of 64.57% and validation loss 0.07 in the model with 46% sensitivity and 64% classification accuracy in testing data.
Date of Award
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Jayasekara Pathiranage, Anuruddha, "Convolutional Neural Networks for Predicting Skin Lesions of Melanoma" (2017). Student Publications. 843.