An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions

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

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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. View Full-Text
Original languageEnglish
Number of pages12
JournalComputers
Volume8
Issue number3
DOIs
Publication statusPublished - 28 Aug 2019

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Skin
Computer aided diagnosis
Support vector machines
Learning systems
Feature extraction
Image processing
Processing
Experiments

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Hameed, Nazia ; Hameed, Fozia ; Shabut, Antesar ; Khan, Sehresh ; Cirstea, Silvia ; Hossain, Alamgir. / An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions. In: Computers. 2019 ; Vol. 8, No. 3.
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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. View Full-Text",
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An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions. / Hameed, Nazia; Hameed, Fozia; Shabut, Antesar; Khan, Sehresh; Cirstea, Silvia; Hossain, Alamgir.

In: Computers, Vol. 8, No. 3, 28.08.2019.

Research output: Contribution to journalArticle

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T1 - An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions

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AU - Hameed, Fozia

AU - Shabut, Antesar

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AU - Cirstea, Silvia

AU - Hossain, Alamgir

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AB - 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

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