Convolutional Neural Network-Based Classification of Skin Lesions Using Dermoscopic Images

Skin cancer is one of the most common and deadliest diseases globally, in which early detection may help improve patient survival rates. In this paper, an automatic skin cancer classification framework based on deep learning using dermoscopic images is presented. Various convolutional neural network (CNN) models were trained and tested on an annotated skin lesion dataset, including MobileNet, EfficientNetB0, VGG16, ConvNet, and ResNet50. Metrics of the models were calculated by using Micro-averaged metrics to assess the general effectiveness for all the classes. ResNet50 obtained the best performance against all tested models with a micro-average accuracy of 97.75%, precision of 97.79%, recall of 97.75%, and F1 score of 97.76%. Our results indicate that the model enables accurate, consistent, and balanced classification of different skin lesion categories, including actinic keratoses, basal cell carcinoma, benign Keratosis-like lesions, dermatofibroma, and melanoma. For real-world utilization, the top performer, the ResNet50 model, was implemented in a Streamlit-based web application, which is designed to automatically predict skin diseases in dermoscopic images that were uploaded. Experimental results show that deep residual learning is effective for improving the classification performance of skin lesions, and it can become an assistive decision-making tool for dermatologists in early diagnosis and clinics.