Automated Detection of Melanoma Skin Disease Using Classification Algorithm

The advancement of modern technology has enabled the diagnosis of various skin diseases through image processing. Researchers face significant challenges when it comes to utilize image processing tools for skin disease analysis. One particularly serious disease is Melanoma, a type of cancer originating from melanocytes. The primary objective of this research article is to develop a machine learning classification-based algorithm for melanoma detection. The complexities associated with analyzing melanoma skin disease can be mitigated by employing an effective classification technique. In this proposed model, two machine learning (ML) classification algorithms, namely the Probabilistic Neural Network (PNN) and Support Vector Machine (SVM), are utilized for disease detection. These algorithms are employed to differentiate between melanoma-affected skin and normal skin. To establish the machine learning algorithms, feature selection is performed using Factor Analysis (FA), and the dermenetNZ.org dataset is utilized. The performance of the two classification algorithms is compared using standard performance metrics. Through comparative analysis, it is demonstrated that the Probabilistic Neural Network classifier outperforms the Support Vector Machine in the classification of melanoma skin disease. Overall, this research article showcases the efficacy of a machine learning-based approach for melanoma detection, with the Probabilistic Neural Network exhibiting superior results compared to the Support Vector Machine classifier.