Shallow Convolutional Neural Network Configurations for Skin Disease Diagnosis

Abstract

Cancer incidence is usually relatively low, skin diseases are not paid enough attention, and most patients, when admitted to the hospital, are in a late state which is already much damage and making it difficult to treat. Some diseases have so many similarities that it is difficult to distinguish between diseases when viewed with the naked eye. Currently, the trends of applying artificial intelligence techniques to support medical imaging diagnosis are vigorously applied and achieved many achievements with deep learning in image recognition. Deep architectures are complex and heavy, while shallow architectures also bring good performance with some appropriate configurations. This study investigates configurations of shallow convolutional neural networks for binary classification tasks to support skin disease diagnosis. Our work focuses on studying and evaluating the effectiveness of simple architectures with high accuracy on the problem of skin disease identification through images. The experiments on eight considered skin diseases have revealed that shallow architectures can perform better on small image sizes (32 $\times$ 32) rather than larger ones (128 $\times$ 128) with more than 0.75 in accuracy on all considered diseases.