Yusra Shafiq

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 classification under similar training conditions.