An Efficient Skin Cancer Diagnostic System Using Bendlet Transform and Support Vector Machine
| Autor: | Ganesh Babu Tr, Poovizhi S |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Support vector machine Skin Neoplasms Support Vector Machine Computer science Feature extraction Dermoscopy multi-resolution analysis 02 engineering and technology Sensitivity and Specificity 020901 industrial engineering & automation Wavelet 0202 electrical engineering electronic engineering information engineering Median filter medicine Curvelet Medical image classification Skin cancer Humans Diagnosis Computer-Assisted lcsh:Science Melanoma medical image classification Multidisciplinary skin cancer integumentary system business.industry Reproducibility of Results Pattern recognition Bendlet Transform medicine.disease Contourlet bendlet transform Shearlet lcsh:Q 020201 artificial intelligence & image processing Artificial intelligence Multi-resolution analysis business |
| Zdroj: | Anais da Academia Brasileira de Ciências v.92 n.1 2020 Anais da Academia Brasileira de Ciências Academia Brasileira de Ciências (ABC) instacron:ABC Anais da Academia Brasileira de Ciências, Vol 92, Iss 1 |
| ISSN: | 1678-2690 |
| Popis: | Skin is the outermost and largest organ of the human body that protects us from the external agents. Among the various types of diseases affecting the skin, melanoma (skin cancer) is the most dangerous and deadliest disease. Though it is one of the dangerous forms of cancer, it has a high survival rate if and only if it is diagnosed at the earliest. In this study, skin cancer classification (SCC) system is developed using dermoscopic images. It is considered as a classification problem with the help of Bendlet Transform (BT) as features and Support Vector Machine (SVM) as a classifier. First, the unwanted information’s such as hair and noises are removed using median filtering approach. Then, directional representation based feature extraction system that precisely classifies curvature, location and orientation is employed. Finally, two SVM classifiers are designed for the classification. The performance of the SCC system based on Bendlet is superior to other image representation systems such as Wavelets, Curvelets, Contourlets and Shearlets. |
| Databáze: | OpenAIRE |
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