Deep Learning Approach for Early Detection and Classification of Skin Lesions

Computer-aided systems for skin lesion diagnostics are an expanding field of study. Recently, researchers have increasingly focused on developing computer-aided diagnosis systems. This paper presents a study on the detection of skin lesions using deep learning techniques. This research focuses on using deep learning models to classify skin lesions while using their images accurately. The study addresses the crucial need for efficient early skin cancer detection methods, which help improve patient recovery. Deep learning models such as convolutional neural networks (CNNs), are used to train models for lesion classification. This research compares the outcomes of training models on unbalanced and balanced datasets. The key findings of this study are the efficiency of this deep learning model in accurately detecting skin lesions. Experimental findings reveal significant enhancements in model accuracy when trained on balanced datasets, with validation accuracy increasing from 76% to 86% and testing accuracy improving from 74% to 83%. These results underscore the potential of deep learning methodologies to revolutionize dermatological diagnostics, paving the way for precise and efficient diagnostic tools for healthcare practitioners.