Melanoma Classification Approach with Deep Learning-Based Feature Extraction Models

Abstract

Melanoma is considered the worst type of skin cancer. The early diagnosis of this disease is still a complex task due to many variables that must be analyzed. Because of this, new methodologies are becoming common in the literature due to the good results obtained. Convolutional Neural Networks are Deep Learning techniques capable of providing effective solutions in the classification of medical images. In this sense, this work developed a disease detection system using AlexNet and VGG-F convolutional architectures, trained with images of skin lesions to create feature descriptors, not classifiers. Other conventional descriptors of skin lesions were used to assess the quality of data obtained from the last layers of convolutional architectures. Data from all feature extraction processes were submitted to the conventional classifiers Support Vector Machine, Multilayer Perceptron, and K-Nearest Neighbor. The results obtained in the approach show that the feature extracting models are viable and can offer a more accurate melanoma diagnosis possibility. The VGG-F architecture obtained the best result, with an accuracy of 91.54% and a precision of 91.64% given by the K-Nearest Neighbor. It is possible to see that this result highlights the quality of data in convolutional architectures and can provide a sense of further research.