Deep Learning-Based Classification of Skin Lesions for Enhanced Dermatological Diagnosis

Skin cancer represents a pervasive global health concern, distinguished by the aberrant growth of skin cells, with ultraviolet (UV) exposure emerging as a prominent catalyst. Early detection is vital for effective treatment and patient outcomes, underscoring the need for precise diagnostic tools and preventive measures. Skin cancer detection through deep learning has garnered significant attention in contemporary research due to its potential for enhancing early diagnosis and treatment outcomes. This paper explores strategies for preventing skin cancer and proposes a novel method for automated skin cancer classification utilizing deep learning techniques. The process involves segmenting regions of interest from skin images and stacking the segmented areas with the original images. Augmented images are then employed to train a deep learning model using transfer learning techniques for the multi-class classification of different skin conditions. The dataset utilized in this study is HAM10000, a well-known repository of dermatoscopic images consisting of 10015 images. The segmentation process with DeepLabV3 and the classification with ResNet152V2 resulted in an accuracy of 98.4%. Despite its promise, the relatively small dataset size presents a limitation, necessitating data augmentation to increase sample diversity. This proposed methodology shows potential for enhancing early identification and improving the accuracy of classifying various skin conditions, thus contributing to better patient outcomes and healthcare effectiveness.