Stand-Alone Melanoma Diagnostic System Using the Raspberry PI 4 (Published)
Skin cancer, in general, is a growing threat to human health. Melanoma skin cancer is one of the most severe types of skin cancer in humans. In the initial stages, melanoma affects the skin in general, and it may develop and spread to other parts of the human body in its advanced stages. Early detection can reduce its severity and improve treatment outcomes. In this study, the proposed system includes image processing techniques and feature extraction by combining Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM), in addition to enhancing the extracted features with the diameter feature. A Support Vector Machine (SVM) model with a radial basis function (RBF) kernel was employed to train and predict the results of the test data on the Raspberry Pi 4 platform. The available International Skin Imaging Collaboration (ISIC) dataset of skin images was used in the training and testing of this system. The test results showed that the proposed system achieves high specificity (99.35%), medium sensitivity (75.35%), and comprehensive accuracy (93.71%).
Keywords: GLCM, LBP, Melanoma, Raspberry Pi 4, SVM