Hybrid Deep Learning Framework for Melanoma Detection: Integrating GANs, Capsule Networks, and CNN + SVM

Melanoma is an aggressive kind of skin cancer, coming under dire need of being detected early for effective treatment. The paper proposes a hybrid deep-learning model that combines the principles of Generative Adversarial Networks (GAN), Capsule Networks (CapsNet), and a CNN-SVM Ensemble model for improved level of melanoma classification. Herein, the GAN is used for data augmentation, while CapsNet provides enhancement concerning spatial feature extraction and, in turn, contributes to passing high-level features to CNN-SVM model ensuring high accuracy in classification. The model is evaluated on a publicly available dataset, outperforming traditional methods with an accuracy of 92% to 96%. This approach shows a promising tool for early detection of melanoma, thus helping dermatologists to make the right diagnosis and conduct appropriate treatment.