aspg-21-1266-1791475399 20261008160319 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Neutrosophic Science IJNS 2692-6148 2690-6805 2022 19 1 Fatma Taher College of Technological Innovation, Zayed University, Dubai, UAE Ahmed Abdelaziz Nova Information Management School, Universidade Nova de Lisboa, 1070-312, Lisboa, Portugal ; Information System Department, Higher Technological Institute, HTI, Cairo 44629, Egypt Early detection and classification of skin lesions using dermoscopic images have attracted significant attention in the healthcare sector. Automated skin lesion segmentation becomes tedious owing to the presence of artifacts like hair, skin line, etc. Earlier works have developed skin lesion detection models using clustering approaches. The advances in neutrosophic set (NS) models can be applied to derive effective clustering models for skin lesion segmentation. At the same time, artificial intelligence (AI) tools can be developed for the identification and categorization of skin cancer using dermoscopic images. This article introduces a Neutrosophic C-Means Clustering with Optimal Machine Learning Enabled Skin Lesion Segmentation and Classification (NCCOML-SKSC) model. The proposed NCCOML-SKSC model derives a NCC-based segmentation approach to segment the dermoscopic images. Besides, the AlexNet model is exploited to generate a feature vector. In the final stage, the optimal multilayer perceptron (MLP) model is utilized for the classification process in which the MLP parameters are chosen by the use of a whale optimization algorithm (WOA). A detailed experimental analysis of the NCCOML-SKSC model using a benchmark dataset is performed and the results highlighted the supremacy of the NCCOML-SKSC model over the recent approaches. 2022 177 187 1266 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJNS.190113 https://www.americaspg.com/journal/21/article/1266 https://www.americaspg.com/storage/6864.pdf