Melanoma Classification using GAN based augmentation and Self-Supervised feature extraction

Melanoma is one of the most severe type of skin cancer caused by the DNA mutations in pigment cells called melanocytes. The current diagnosing methods involves visually examining lesion images which makes it time consuming. This makes it an active area of research which is being pursued using various deep-learning models. However, classification models that are developed using limited labeled training data might have a negative impact on the diagnostic procedure. This study presents a self-supervised learning-based model trained on dermoscopic images for automated melanoma recognition, in which feature extraction is accomplished using unlabeled data, and further fine-tuning is done using labeled data for classification. Additionally, an intermediary step is included for augmentation using Deep Convolution Generative Adversarial Networks (DCGAN) to enhance the number of unlabeled images for the pretext learning step. The trained model improved the accuracy by 4% as compared to just training using the original dataset. The proposed method can be extended to a broader range of applications in the medical area, where there is a real scarcity of data to train models.