An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions

Hameed, Nazia; Hameed, Fozia; Shabut, Antesar; Khan, Sehresh; Cirstea, Silvia; Hossain, Alamgir

Abstract

Skin diseases cases are increasing on a daily basis and are dicult 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.

Citation

Hameed, N., Hameed, F., Shabut, A., Khan, S., Cirstea, S., & Hossain, A. (2019). An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions. Computers, 8(3), Article 62. https://doi.org/10.3390/computers8030062

• Journal Article Type: Article
• Acceptance Date: Aug 26, 2019
• Online Publication Date: Aug 28, 2019
• Publication Date: Aug 28, 2019
• Deposit Date: Sep 26, 2019
• Publicly Available Date: Sep 26, 2019
• Journal: Computers
• Electronic ISSN: 2073-431X
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 8
• Issue: 3
• Article Number: 62
• DOI: https://doi.org/10.3390/computers8030062
• Public URL: https://nottingham-repository.worktribe.com/output/2663266
• Publisher URL: https://www.mdpi.com/2073-431X/8/3/62
• Contract Date: Sep 26, 2019