Classification of Skin Cancer Dermoscopy Images using Transfer Learning

Cancer is the second death causing disease in the world. Skin cancer is a major type of cancer and increasing speedily over the past decades. The major causes of skin cancer are Ultraviolet radiation, smoking, DNA mutation and bad lifestyle. Skin cancer is diagnosed by employing clinical screening, dermoscopic analysis, histopathological examination and a biopsy. Early prognosis of skin cancer can help to cure cancer easily and It is a difficult task to diagnosis and classify skin cancer even for expert dermatologists at an early stage. Recent studies have shown that deep learning and transfer learning is very useful in the classification of skin cancer and medical diagnosis. In this paper, we propose an efficient way to classify skin cancer using deep learning. We have tuned the pre-trained MobileNet convolution neural network and trained over the HAM1000 skin lesion dataset. This transfer learning method has shown remarkable categorical accuracy, the weighted average of precision and recall 0.97, 0.90 and 0.91 respectively. This model is lightweight, fast and reliable. It will be helpful for dermatologists to prognosis the skin cancer at its early stage.