Mitigating Biases in Dermatological Diagnosis through GAN Augmentation and Classification

Skin conditions affect individuals of all skin tones, yet biases in dermatological diagnosis disproportionately impact those with darker skin, leading to significant healthcare disparities. This paper proposes a comprehensive solution that integrates advanced data preprocessing, Generative Adverserial Network (GAN) augmentation, and Convolutional Neural Network (CNN) models to mitigate these biases. The initial phase involves meticulous preprocessing of diverse dermatological image datasets, ensuring broad coverage of skin tones. Images are standardized, noise is removed, and critical features are preserved for accurate classification. To address data imbalance, particularly for underrepresented skin tones, a GAN with spectral normalization generates synthetic images of dark skin lesions, enriching the dataset and enhancing model generalization. The preprocessed and augmented dataset is used to train three CNN architectures are ResNet50, NASNetLarge, and InceptionResNetV2, each fine-tuned to optimize performance. Class weights handle imbalances, and an ensemble approach improves classification accuracy, particularly in detecting challenging conditions like melanoma across all skin tones. This combined approach of GAN augmentation and CNN-based classification offers a promising path toward more equitable dermatological diagnostics.