Dermoscopic assisted diagnosis in melanoma: Reviewing results, optimizing methodologies and quantifying empirical guidelines

Early diagnosis is still the most important factor to deal with skin cancer, a disease that challenges physicians and researchers. It has benefited from computer-aided diagnosis methods that successfully combine dermoscopy, Digital Image Processing, and Machine Learning techniques. This paper aims to approximate medical professionals working with dermoscopy to these methods, to join the challenge of melanoma early detection. Accordingly, a proposal for extracting, selecting and combining texture and shape features from dermoscopic images is presented. The Feature Selection task is added to the learning process to potentiate the quality of classification models. Three classical Machine Learning algorithms were applied to differentiate melanoma from non-melanoma images. The models are evaluated by standard performance measures and a multi-criteria decision analysis method. This is the first time such method is used in melanoma diagnosis. As a result, we found a decision tree that performs well and allows the explicit representation and analysis of the knowledge learned from the images. In addition, the competitiveness of our decision models in comparison with literature approaches reviewed in this work encourages further applications of Machine Learning and Feature Selection to assist computer-aided diagnosis.