Neutrosophic C-Means Clustering with Optimal Machine Learning Enabled Skin Lesion Segmentation and Classification

Document Type

Article

Source of Publication

International Journal of Neutrosophic Science

Publication Date

1-1-2022

Abstract

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 det ection 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.

ISSN

2692-6148

Publisher

American Scientific Publishing Group

Volume

19

Issue

1

First Page

177

Last Page

187

Disciplines

Computer Sciences | Medicine and Health Sciences

Keywords

Feature Extraction, Image segmentation, Machine learning, Neutrosophic set, Whale optimization algorithm

Scopus ID

85139564362

Indexed in Scopus

yes

Open Access

no

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