Deep Convolutional Neural Network for Melanoma Image Classification

Melanoma is the most aggressive of all skin cancers and its incidence has reached epidemic proportions. It is important to distinguish between benign and malignant melanoma as early as possible to increase the chance of recovery. The development of computational technology, especially machine learning and computer vision, made it possible to classify diseases based on their image. Detection of a disease by using image is beneficial because it can be done more easily, cheaply, quickly, and non-invasively than by using biopsy. The use of conventional machine learning and computer vision method makes their classification performance highly affected by the segmentation result of the skin lesion and the features selected for the classification process. The recent development of deep learning algorithm, such as CNN (Convolutional Neural Network), makes it possible to classify images without going through the process of image segmentation and manual features determination and give high performance with enough training data. Therefore, in this research we propose a deep convolutional neural network (CNN) to classify melanoma images into benign and malignant class. The proposed network architecture consists of several sets of convolutional layers and max-pooling layers, followed by a drop out layer and a fully-connected layer. From the experimental results on 352 test images, the proposed network gives the accuracy, sensitivity, and specificity of 84.76%, 91.97%, and 78.71%. The good performance of the built model hopefully can be developed for real application that can assist the expert to make better diagnosis and treatment.