Skin Cancer Classification with CGAN-Based Data Augmentation

Detection of skin cancer remains a crucial medical issue because it determines the effectiveness of melanoma treatment. The current detection systems experience performance limitations because of limited labeled data which results in overfitted models that produce narrow potential outcomes while demonstrating poor generalization for unknown skin lesion classes. This research proposes solving classification challenges through the implementation of Conditional Generative Adversarial Networks which produces synthetic images that replicate the natural variability seen in real-world skin lesion scans. Synthetic images from CGANs enhance CNN training sets while improving their capabilities to identify various types of skin cancer. The proposed system exists as a platform which trains CGANs on real skin lesion datasets to produce matching synthetic imagery that amalgamates with original datasets before creating an extended CNN training set. The evaluation of proposed CNN models uses real skin lesion images with their performance evaluated through accuracy and sensitivity while measuring specificity and F1 score metrics. Model performance improves when training augmentation techniques are used instead of original image sets resulting in enhanced robustness and precision together with generalized results. Additional incorporation of synthesized data leads to substantial advancement in detecting skin conditions which dermatologists identify rarely. The research has established CGANs as promising tools for generating synthetic medical images which address data deficit challenges while showing foundationally that data augmentation strengthens deep learning model capabilities.