Advanced Skin Lesion Classification Using Hybrid Deep Learning with Explainable AI

Abstract

Skin cancer continues to grow as a major global health problem, therefore early identification is vital for the successful treatment and reduction of mortality associated with skin cancer. The current methods for diagnosing skin cancer require a long time-period and high-costs, and rely on the skill of dermatologists. These factors contribute to delayed diagnosis, limited access to specialization, and inconsistent results. To improve these challenges; this research proposes an AI-based automated system for detecting skin cancer through deep-learning algorithms. The proposed system utilizes VGG16 Convolutional Neural Network (CNN) which uses a pre-trained model (VGG16) as a feature extractor for dermatological images. The images will be processed using image-presentation techniques including noise removal and image-normalization in order to enhance the quality of the images so as to allow effective extraction of features.Such important features like colour, shape, and texture will be extracted from the dermatological images through the VGG16 model in a manner so that the results can be categorised into 7 different categories of types of skin cancers. The results will then allow the healthcare professional and the user to make informed medical decisions, and to obtain timely treatment options. This study provides evidence that deep learning can be successfully integrated into automated medical imaging to help detect skin cancer at an early stage, thus providing a cost-effective method for delivering dermatological care. In addition, this study highlights ways to maximize the use of available resources in dermatology, improve the accuracy of diagnoses and foster better access to healthcare services.