An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions

Hameed, Nazia and Hameed, Fozia and Shabut, Antesar M. and Khan, Sehresh and Cirstea, Silvia and Hossain, Mohammed Alamgir (2019) An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions. Computers, 8 (3). p. 62. ISSN 2073-431X

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Official URL: http://dx.doi.org/10.3390/computers8030062

Abstract

Skin diseases cases are increasing on a daily basis and are difficult to handle due to the global imbalance between skin disease patients and dermatologists. Skin diseases are among the top 5 leading cause of the worldwide disease burden. To reduce this burden, computer-aided diagnosis systems (CAD) are highly demanded. Single disease classification is the major shortcoming in the existing work. Due to the similar characteristics of skin diseases, classification of multiple skin lesions is very challenging. This research work is an extension of our existing work where a novel classification scheme is proposed for multi-class classification. The proposed classification framework can classify an input skin image into one of the six non-overlapping classes i.e., healthy, acne, eczema, psoriasis, benign and malignant melanoma. The proposed classification framework constitutes four steps, i.e., pre-processing, segmentation, feature extraction and classification. Different image processing and machine learning techniques are used to accomplish each step. 10-fold cross-validation is utilized, and experiments are performed on 1800 images. An accuracy of 94.74% was achieved using Quadratic Support Vector Machine. The proposed classification scheme can help patients in the early classification of skin lesions.

Item Type: Journal Article
Keywords: multi-class skin lesions classification, melanoma classification, acne classification, eczema classification, psoriasis classification, automated classification, skin disease classification
Faculty: Faculty of Science & Engineering
SWORD Depositor: Symplectic User
Depositing User: Symplectic User
Date Deposited: 09 Oct 2019 09:42
Last Modified: 14 Nov 2019 16:07
URI: http://arro.anglia.ac.uk/id/eprint/704850

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