Diagnosis of Melanoma Based on the Sparse Auto-Encoder for Feature Extraction

Abstract

Aims: Skin cancer is a fairly critical disease all over the world and especially in Western countries and America. However, if it is perceived and treated early, it is quite often curable. The main risk factors for melanoma are exposure to UV rays, the presence of many moles, and heredity. For this reason, this work focuses on the issue of automatic diagnosis of melanoma. The aim is to extract significant features from pixels of the images based on an unsupervised deep learning technique which is the sparse autoencoder method. Methodology: A preprocessing phase is required to remove the artifacts and enhance the contrast of the images before proceeding with the feature extraction. Once the characteristics are extracted automatically, the support vector machine classifier and the k-nearest neighbors are applied for the classification phase. The objective is to differentiate between 3 categories: melanoma, suspected case, and non-melanoma. Finally, the PH2 database is used to test the proposed approaches (200 images are presented in this dataset: 80 atypical nevi, 80 common nevi, and 40 melanoma). Results: The obtained results in terms of specificity, accuracy, and sensitivity present noticeable performances with the support vector machine classifier (achieved 94 % overall accuracy) and the k-nearest neighbors (92 %). Conclusion: This study's experimental findings showed that the best performance was obtained by the approach based on a deep sparse autoencoder combined with support vector machine.