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
Musical Genre Classification of Recorded Songs based on Music Structure Similarity (Published)
Automatic music genre classification is a research area that is increasing in popularity. Most researchers on this research area have been focusing on combining information from different sources than the musical signal itself. This paper presents a novel approach for the automatic music genre classification problem using audio signal for the context of Sri Lankan Music. The proposed approach uses two feature vectors and Support Vector Machine (SVM) classifier with radial-basis kernel function. More specifically, two feature sets for representing frequency domain, temporal domain, cepstral domain and modulation frequency domain audio features are proposed through this work. Music genre classification accuracy of 74.5% was recorded as the highest overall classification accuracy on our dataset containing over 100 songs over the five musical genres. This approach shows that it is possible to implement a genre classification model with a reasonably good accuracy by using different types of domain based audio features.
Keywords: Audio Signal Analysis, Feature Extraction, Music Information Retrieval, Musical Genre Classification, SVM