抽象的な

A hybrid segmentation approach for detection and classification of skin cancer

Akila Victor, Muhammad Rukunuddin Ghalib

Advancement in Computer Aided Diagnostic system (CAD) enhances the detection and classification of domain experts and reduces the time rapidly for them. The CAD systems can be used in hospitals as an alternate method. The objective of the paper is to present the effectiveness of the detection and classification of skin cancer. The proposed methodology concentrates on comparing the median filter and Adaptive Median Filter (AMF) and suggesting on one, the segmentation can be done by a hybrid approach where the marker controlled watershed algorithm is fused with the active contour algorithm, the feature extraction is done with the help of basic statistical methods and the Grey Level Co- Occurrence Matrix (GLCM) with the Support Vector Machine (SVM) for classification. SVM is used to classify the input as cancerous or not. The experiment is carried out on 250 images consists of 100 normal images and 150 abnormal images (benign and melanoma images) from a skin dataset. The classification accuracy shows 94% after the classification.