Hybrid VGG-SVM Framework for Melanoma Detection: Integrating GAN-Augmented Data and LIME Interpretability

Melanoma is a severe form of skin cancer that requires early detection for effective treatment. This study explores the use of advanced AI techniques to enhance the accuracy of melanoma diagnosis. We proposed a hybrid VGG-SVM model that incorporates GAN-based data augmentation to enhance the training process. Additionally, we used LIME (Local Interpretable Model-agnostic Explanations) to gain insights into the model's decision-making. By training on large datasets of dermatoscopic images, our method aimed to detect subtle melanoma characteristics with high accuracy while maintaining explainability. The proposed VGG-SVM model, combined with LIME, achieved an accuracy, precision, and F1-score of 96%, surpassing previous state-of-the-art methods. This significant improvement in both accuracy and interpretability addresses a critical scientific gap in AI-assisted melanoma diagnosis, potentially enhancing early detection rates and clinical trust in AI systems. Our study demonstrates the potential of hybrid AI models in medical image analysis and highlights the importance of interpretability in clinical applications. Future work will focus on clinical validation and exploration of transfer learning techniques to further improve model performance across diverse patient populations.