Classification of Skin Cancer Images Using Convolutional Neural Networks

Abstract

Skin cancer is the most common human malignancy according to American Cancer Society. It is primarily diagnosed visually, starting with an initial clinical screening and followed potentially by dermoscopic (related to skin) analysis, a biopsy and histopathological examination. Skin cancer occurs when errors (mutations) occur in the DNA of skin cells. The mutations cause cells to grow out of control and form a mass of cancer cells. The aim of this study was to try to classify images of skin lesions with the help of Convolutional Neural Networks. Deep neural networks show humongous potential for image classification while taking into account the large variability exhibited by the environment. Here, we trained images on the basis of pixel values and classified them on the basis of disease labels. The dataset was acquired from an Open Source Kaggle Repository (Kaggle Dataset) which itself was acquired from ISIC (International Skin Imaging Collaboration) archive. The training was performed on multiple models accompanied with Transfer Learning. The highest model accuracy achieved was over 86.65%. The dataset used is publicly available to ensure credibility and reproducibility of the aforementioned result.