Classification of breast abnormality using decision tree based on GLCM features in mammograms. (2018)

• Record Type: Journal Article
• Authors: Kamalakannan, J.; Babu, M. Rajasekhara
• Is Part Of: International journal of computer aided engineering and technology. Volume 10:Number 5(2018)
• Page Start: 504
• Page End: 512
• Publication Date: 2018
• Subjects: mammogram -- screening -- feature -- grey level co-occurrence matrices -- GLCM -- malignant -- benign -- medical imaging -- screening -- features -- MIAS; Computer-aided engineering -- Periodicals; 620.00285
• Languages: English
• ISSNs: 1757-2657

Abstract

Breast cancer is the second most common cancer among the women and the major victim for the breast cancer is the women. In the USA, one out of eight is diagnosed as breast cancer among the other cancers. Medical images can be analysed for identification. Image pre-processing is an essential procedure used for reducing image noise, highlighting edges, or displaying digital images. Mammogram is the best way for screening the breast. Applying medical image techniques could help in identifying and classifying the abnormalities present in the breast. The features which are extracted from medical images can also be given as input to the classifier for classification. Mammogram has been given as input to the proposed system. Mammograms are pre-processed before given to the classifier. The features are extracted through GLCM and then decision tree classifier is used in this paper for classifying the breast abnormality as benign and malignant.