International Journal of Neutrosophic Science IJNS 2690-6805 2692-6148 10.54216/IJNS https://www.americaspg.com/journals/show/2421 2020 2020 Sociology Program, Department of Social Sciences, College of Arts and Sciences, Qatar University, P.O. Box 2713, Doha, Qatar Ahmed Ahmed Department of Computer Science, College of Computing and Information Technology, University of Bisha, Bisha 61922, Saudi Arabia Ahmed H. Samak Department of Mathematics, College of Science and Humanities in Alkharj, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia Kottakkaran Sooppy Nisar Department of Electrical Engineering, College of Engineering, University of Business and Technology, Jeddah, 21448, Saudi Arabia Ali Elrashidi Health Information Management Department, Liwa College of Technology, Abu Dhabi, United Arab Emirates Amina Toumi Department of Mathematics, Faculty of Sciences and Information Technology, Jadara University, Irbid, Jordan. Shawkat Alkhazaleh Department of Computer Science, College of Science and Arts in Gurayat, Jouf University, Saudi Arabia Afrah S. Albalawi Department of Computer Science, College of Science and Arts in Gurayat, Jouf University, Saudi Arabia; Information System Department, Faculty of Computers and Information, Assiut University, Assiut, Egypt Rasha M. Abd El El-Aziz In this study, a thorough methodology is used to present a unique way for improving skin cancer prediction accuracy. The research uses sophisticated preprocessing methods, such as the Frost filter for noise reduction and histogram equalization for contrast enhancement, to boost contrast on dermoscopic pictures from various sources, using an ISIC 2020 dataset. These actions greatly raise the dermoscopic pictures’ overall quality and usefulness for diagnosis. Utilizing labeled data for training, we offer a Fuzzy-based C-means clustering technique based on Neutrosophic Logic during the segmentation phase. In order to overcome ambiguities in skin lesion segmentation, the neutrosophic set—a groundbreaking idea in philosophy—is used. The suggested model enhances the accuracy of segmentation by modifying the neutrosophic set functions. For precise prediction, the approach combines Support Vector Machine (SVM) classification with Histogram of Oriented Gradient (HOG) feature extraction. While SVM, a supervised learning algorithm, diagnoses skin lesions based on the collected features, HOG features capture gradient information. To improve object recognition and classification, the HOG-SVM architecture is made to methodically collect and quantify essential information using dermoscopic pictures. The use of Neutrosophic Fuzzy Logic, which combines the benefits of fuzzy clustering with neutrosophic sets to produce more precise and nuanced predictions, sets the suggested method apart. The integration of different approaches into a holistic solution for skin cancer prediction is what makes the proposed study innovative. Findings and performance analysis show of the HOG-SVM method exhibits an outstanding accuracy of 98.69%, outperforming LR, KNN, and GNB methods. Python software is used to accomplish the suggested approach. This discovery opens up a possible path for better skin cancer diagnosis and advances the rapidly developing fields of dermatology and medical image processing. 2024 2024 317 334 10.54216/IJNS.230226 https://www.americaspg.com/articleinfo/21/show/2421