DEEP TRANSFER LEARNING FOR DERMOSCOPIC SKIN LESION CLASSIFICATION: BENCHMARKING XCEPTION, INCEPTIONRESNETV2, MOBILENETV3LARGE, DENSENET121, AND NASNETMOBILE
This research proves that transfer learning, systematic class balancing, and focal loss functions provide a computationally viable approach and highly effective method for automatic skin cancer classification, while also highlighting that the backbone architecture is the key factor that determines the effectiveness of...