Multi- classification of skin lesions using a deep learning-based convolutional neural network

Abstract

Skin lesions refer to changes or abnormalities on the skin, such as moles, freckles, cysts, warts, and more. While not all skin lesions are cancerous, some types can indicate or develop into skin cancer. Skin cancer has been on the rise in recent years, causing many deaths. However, manual examination by dermatologists is expensive and time-consuming. Research in this field has focused on developing an automated system to detect skin cancer and reduce the associated mortality rate. However, the performance of such systems needs improvement. In this study, we propose a Synthetic Minority Over-sampling Technique combined with Edited Nearest Neighbors (SMOTEENN) to augment the data, resulting in a more balanced dataset. Then, we used a Convolutional Neural Network (CNN) model on the benchmark HAM10000 dataset. The dataset comprises seven skin diseases, including Actinic Keratoses, Basal cell carcinoma, Benign keratosis, Dermatofibroma, Melanocytic nevi, Melanoma, and Vascular skin lesions. With a balanced dataset, the proposed model achieves an accuracy, precision, recall, and f1-score of 99.35%, 99.00%, 99.00%, and 99.00%, respectively. The balanced dataset performs 22% better than an unbalanced dataset.