An Efficient Skin Cancer Diagnostic System Using Bendlet Transform and Support Vector Machine



Título del documento: An Efficient Skin Cancer Diagnostic System Using Bendlet Transform and Support Vector Machine
Revista: Anais da Academia Brasileira de Ciencias
Base de datos: PERIÓDICA
Número de sistema: 000436206
ISSN: 0001-3765
Autores: 1
2
Instituciones: 1Anna University, Department of Electronics and Communication Engineering, Chennai, Tamil Nadu. India
2Muthayammal Engineering College, Department of Electronics and Communication Engineering, Chennai, Tamil Nadu. India
Año:
Volumen: 92
Número: 1
País: Brasil
Idioma: Inglés
Tipo de documento: Artículo
Enfoque: Aplicado, descriptivo
Resumen en inglés 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
Disciplinas: Medicina
Palabras clave: Oncología,
Diagnóstico,
Cáncer de piel,
Imágenes médicas,
Análisis de multirresolución,
Transformada de Bendlet,
Máquinas de soporte vectorial
Keyword: Oncology,
Diagnosis,
Bendlet Transform,
Medical images,
Multiresolution analysis,
Skin cancer,
Support vector machines
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