Mammogram tumour classification using modified segmentation techniques Online publication date: Sat, 27-Sep-2014
by D. Sriramkumar; R. Malmathanraj; R. Mohan; S. Umamaheswari
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 13, No. 3, 2013
Abstract: Sophisticated technologies in the field of medical electronics have helped to improve the lives of patients. In this work, an intelligent system for the identification of micro-calcification clusters in digitised mammograms is proposed. The proposed intelligent system consists of two stages: segmentation algorithm and a neural network/kernel system. The system was tested for mammogram images for the Mammographic Image Analysis Society and Digital Database for Screening Mammographic Databases. Different classifiers such as support vector machines, learning vector quantiser, radial basis function and back propagation neural network are used and tested. Support vector machine classifier outperforms other classifiers. The proposed system incorporates improved segmentation and better classification. The proposed intelligent system gives a better accuracy. The execution time is considerably low-befitting real-time applications. The application of the system can be used to identify tumours as benign or malignant.
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