A Convolutional Neural Network based system for classifying malignant and benign skin lesions using mobile-device images

Abstract

The 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 and upward trend. To address this issue, we propose a skin lesion classification model that leverages EfficientNet 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 Nevus(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%