Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning

-- Recent advances in deep learning have significantly improved the accuracy of skin lesion classification models, supporting medical diagnoses and promoting equitable healthcare. However, concerns remain about potential biases related to skin color, which can impact diagnostic outcomes. Ensuring fairness is challenging due to difficulties in classifying skin tones, high computational demands, and the complexity of objectively verifying fairness. To address these challenges, we propose a fairness algorithm for skin lesion classification that overcomes the challenges associated with achieving diagnostic fairness across varying skin tones. By calculating the skewness of the feature map in the convolution layer of the VGG (Visual Geometry Group) network and the patches and the heads of the Vision Transformer, our method reduces unnecessary channels related to skin tone, focusing instead on the lesion area. This approach lowers computational costs and mitigates bias without relying on conventional statistical methods. It potentially reduces model size while maintaining fairness, making it more practical for real-world applications. In recent years, the predictive performance of deep learning models for skin lesion classification has improved significantly [1], [2]. These models have the potential to assist medical professionals in diagnosing diseases more efficiently, allowing for timely medical diagnosis. Furthermore, they can enable mobile and web-based self-diagnosis tools, improving access to healthcare and promoting equitable delivery in low-and middle-income regions. However, despite these advances, concerns persist that algorithmic and data biases can compromise fairness in healthcare [3], [4].