Towards Improving Skin Cancer Detection Using Transfer Learning

Abstract

In present time, skin cancer is the deadliest disease among humans. In US, two persons die every hour owing to skin cancer. Skin cancer is developed on the body when exposed to sunlight and is the abnormal growth of the skin cell. The patient's life can be saved through earlier and faster detection of skin cancer. The formal method of skin cancer detection is Biopsy, it is done by removing the skin cells and testing the samples in a clinical lab. Biopsy method is invasive and time-consuming. With the newer technologies, early detection of skin cancer at the initial stage is possible. Image processing techniques are instrumental in the health care industry to detect abnormalities in the human body. In this work, Convolutional Neural Network (CNN) algorithm with four different transfer learning techniques are used to classify the images of the skin with dermoscopic analysis which enables fast detection. A CNN model is trained using a dataset of 3700 clinical images and its performance is tested over 660 images which represent the identification of deadliest skin cancer. A considerable improvement in accuracy of skin cancer detection using deep learning architecture ResNet34 provides a reliable approach for early detection and treatment.