A Systematic Investigation on Deep Architectures for Automatic Skin Lesions Classification

Computer vision-based techniques are more and more employed in healthcare and medical fields nowadays, principally, as a support tool to medical staff in order to help them making quick and correct diagnosis. One of the hot topics in this arena concerns the automatic classification of skin lesions. Several promising works have been proposed in the last couple of years, mainly leveraging Convolutional Neural Networks (CNN). However, the proposed pipeline mainly rely on complex data pre-processing and there is no systematic investigation about how available deep models can actually reach the accuracy needed for real applications. In order to overcome these drawbacks, in this work, an end-to-end pipeline is introduced and some of the most recent Convolutional Neural Networks (CNNs) architectures are included in it and compared on the largest common benchmark dataset recently introduced. To this aim, for the first time in this application context, a new network design paradigm, namely RegNet, has been exploited to get the best models among a population of configurations. The paper introduces a threefold level of contribution and novelty with respect to the literature: the deep investigation of several CNN architectures driving to a consistent improvement of the lesions recognition accuracy, the exploitation of a new network design paradigm able to study the behavior of populations of models and a deep discussion about pros and cons of each analyzed method by paving the path towards new research lines.