Author:
Mhedbi Rim,Chan Hannah O.,Credico Peter,Joshi Rakesh,Wong Joshua N.,Hong Collin
Abstract
AbstractThe escalating incidence of skin lesions, coupled with a scarcity of dermatologists and the intricate nature of diagnostic procedures, has resulted in prolonged waiting periods. Consequently, morbidity and mortality rates stemming from untreated cancerous skin lesions have witnessed an upward trend. To address this issue, we propose a skin lesion classification model that leverages the efficient net B7 Convolutional Neural Network (CNN) architecture, enabling early screening of skin lesions based on camera images. The model is trained on a diverse dataset encompassing eight distinct skin lesion classes: Basal Cell Carcinoma (BCC), Squamous Cell Carcinoma (SCC), Melanoma (MEL), Dysplastic Nevi (DN), Benign Keratosis-Like lesions (BKL), Melanocytic Nevi (NV), and an ‘Other’ class. Through multiple iterations of data preprocessing, as well as comprehensive error analysis, the model achieves a remarkable accuracy rate of 87%.
Publisher
Cold Spring Harbor Laboratory
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