文章基本信息

标题：Skin Lesion Analysis for Melanoma Detection Using the Novel Deep Learning Model Fuzzy GC-SCNN
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作者：Usharani Bhimavarapu ; Gopi Battineni
期刊名称：Healthcare
电子版ISSN：2227-9032
出版年度：2022
卷号：10
期号：5
DOI：10.3390/healthcare10050962
语种：English
出版社：MDPI Publishing
摘要：Melanoma is easily detectable by visual examination since it occurs on the skin’s surface. In melanomas, which are the most severe types of skin cancer, the cells that make melanin are affected. However, the lack of expert opinion increases the processing time and cost of computer-aided skin cancer detection. As such, we aimed to incorporate deep learning algorithms to conduct automatic melanoma detection from dermoscopic images. The fuzzy-based GrabCut-stacked convolutional neural networks (GC-SCNN) model was applied for image training. The image features extraction and lesion classification were performed on different publicly available datasets. The fuzzy GC-SCNN coupled with the support vector machines (SVM) produced 99.75% classification accuracy and 100% sensitivity and specificity, respectively. Additionally, model performance was compared with existing techniques and outcomes suggesting the proposed model could detect and classify the lesion segments with higher accuracy and lower processing time than other techniques.
关键词：enfuzzy logicGrabCutconvolution neural networksupport vector machineskin lesion