Neutrosophic Fuzzy Logic based SVM approach for Enhanced Skin Cancer Prediction
Authors:   Sociology Program, Department of Social Sciences, College of Arts and Sciences, Qatar University, P.O. Box 2713, Doha, Qatar, Bedair Khaled ,   Department of Computer Science, College of Computing and Information Technology, University of Bisha, Bisha 61922, Saudi Arabia,   Department of Mathematics, College of Science and Humanities in Alkharj, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia,   Department of Electrical Engineering, College of Engineering, University of Business and Technology, Jeddah, 21448, Saudi Arabia,   Health Information Management Department, Liwa College of Technology, Abu Dhabi, United Arab Emirates,   Department of Mathematics, Faculty of Sciences and Information Technology, Jadara University, Irbid, Jordan.,   Department of Computer Science, College of Science and Arts in Gurayat, Jouf University, Saudi Arabia,   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, Samak Ahmed H. , Nisar Kottakkaran Sooppy , Elrashidi Ali , Toumi Amina , Alkhazaleh Shawkat , Albalawi Afrah S. , El-Aziz Rasha M. Abd El 
Published: January 2024

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.