Melanoma Detection Using Regular Convolutional Neural Networks

In this paper, we propose a method for classifying melanoma images into benign and malignant using Convolutional Neural Networks (CNNs). Having an automated method for melanoma detection will assist dermatologists in the early diagnosis of this type of skin cancer. A regular convolutional network employing a modest number of parameters is used to detect melanoma images. The architecture is used to classify the dataset of the ISBI 2016 challenge in melanoma classification. The dataset was not segmented or cropped prior to classification. The proposed method was then evaluated for accuracy, sensitivity and specificity. Comparisons with the winning entry in the competition demonstrate that one can achieve a performance level comparable to state-of-the-art using standard convolutional neural network architectures that employ a lower number of parameters.