An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions
| Autor: | Fozia Hameed, Alamgir Hossain, Silvia Cirstea, Antesar M. Shabut, Sehresh Khan, Nazia Hameed |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer Networks and Communications
Computer science eczema classification Feature extraction Image processing CAD 02 engineering and technology lcsh:QA75.5-76.95 030207 dermatology & venereal diseases 03 medical and health sciences 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering medicine Segmentation melanoma classification Acne Disease burden business.industry psoriasis classification automated classification Pattern recognition skin disease classification medicine.disease Human-Computer Interaction Support vector machine Computer-aided 020201 artificial intelligence & image processing Artificial intelligence lcsh:Electronic computers. Computer science multi-class skin lesions classification business acne classification |
| Zdroj: | Computers, Vol 8, Iss 3, p 62 (2019) Computers Volume 8 Issue 3 |
| ISSN: | 2073-431X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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