Classification of Skin Lesion Using Image Processing and ResNet50

One of the most critical concerns of healthcare is cancer and the prevalence of skin cancer in the population has been increasing gradually and ominously. In this paper, we have proposed a deep learning model using a pre-trained neural network architecture called ResNet-50, and classify the dermoscopic images collected from the International Skin Imaging Collaboration (ISIC) archive into either benign or malignant. Our objective here is to make the medical diagnosis of skin cancer much more cost-effective and as efficient as possible. First, the input images have to undergo image preprocessing to remove unwanted noise, then we perform image augmentation to increase the number of images. Next, by implementing Otsu’s method for thresholding coupled with masking using bitwise AND operation, we segment the lesion portion of the image. Our model acquired satisfying results with an accuracy of about 85% and the AUC (Area Under The Curve)–ROC (Receiver Operating Characteristics) value of about 0.852. The precision, recall and F1-score values for benign class stand at around 0.89, 0.82 and 0.85 respectively, whereas, for malignant class, it is about 0.80, 0.88 and 0.84 respectively.