Deep Learning for Skin Disease Detection

With skin cancer rates increasing, it remains a significant public health issue. Early detection is crucial for successful treatment. Despite recent strides in Artificial Intelligence (AI) applications for skin lesion classification, these methods often grapple with accuracy limitations. Deep Convolutional Neural Networks (CNNs) have emerged as a promising area for improving the classification accuracy. This study introduces a novel approach leveraging fine-tuning techniques applied to the Xception pretrained model specifically tailored for skin lesion classification. By augmenting the HAM10000 dataset, which comprises diverse lesions across seven classes, and incorporating advanced augmentation strategies, this study aims to mitigate data imbalance effects and enhance the robustness of the proposed model. Furthermore, this study conducts a thorough comparative analysis, benchmarking the proposed approach against state-of-the-art methods to evaluate its efficacy and reliability. The research findings indicate that the proposed fine-tuned Xception model exhibits superior efficiency and reliability compared to previous models, achieving an impressive accuracy of 96%. The augmentation approach, combined with fine-tuning, enables the proposed model to achieve enhanced classification accuracy. The proposed approach shows the potential in enabling precise and timely skin lesion diagnosis, enhancing patient outcomes and advancing AI-driven healthcare.