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Open AccessArticle

An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions

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School of Computer Science, University of Nottingham, Jubilee Campus, Nottingham NG8 1BB, UK
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Computer Science Department, King Khalid University, Abha 61421, Saudi Arabia
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School of Arts and Communication, Leeds Trinity University, Leeds LS18 5HD, UK
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Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Karachi 75600, Pakistan
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Faculty of Science and Engineering, Anglia Ruskin University, Cambridge CB1 1PT, UK
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School of Computing and Digital Technology, Teesside University, Middlesbrough TS1 3BX, UK
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Author to whom correspondence should be addressed.
Computers 2019, 8(3), 62; https://doi.org/10.3390/computers8030062
Received: 19 July 2019 / Revised: 22 August 2019 / Accepted: 26 August 2019 / Published: 28 August 2019
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. View Full-Text
Keywords: multi-class skin lesions classification; melanoma classification; acne classification; eczema classification; psoriasis classification; automated classification; skin disease classification multi-class skin lesions classification; melanoma classification; acne classification; eczema classification; psoriasis classification; automated classification; skin disease classification
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Hameed, N.; Hameed, F.; Shabut, A.; Khan, S.; Cirstea, S.; Hossain, A. An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions. Computers 2019, 8, 62.

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