Comparative analysis on evaluation results of Case Base Reasoning Classification and ANN classification on mammogram mass detection

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Title: Comparative analysis on evaluation results of Case Base Reasoning Classification and ANN classification on mammogram mass detection
Author(s): Raman, Valliappan; Then, Patrick; Sumari, Putra
Abstract: Breast cancer is the most common cancer among women in the western world and in Malaysia, other than skin cancer. It is the second leading cause of cancer death in women, after lung cancer. Screening mammography is currently the most effective tool for early detection of breast cancer. For every thousand cases analyzed by a radiologist, only 3 to 4 are cancerous and the rest could be overlooked. The challenges basically come from the (1) A large variability in the appearance of abnormalities (masses) within digital mammogram made very difficult in image analysis (2) Presence of high dense breast tissues for fibroglandular tissue predominance such as mammogram of young woman which makes the distinction between normal glandular tissues and malignant disease is difficult, So it is very hard to detect the masses with an opaque uniform background and (3) hard to obtain data in an unstructured knowledge of previous screening history. In this paper, we have proposed an algorithm by CBR (Case Based Reasoning Classification) based computerized mammogram mass detection for improving the accuracy rate of detection; the main aim of the paper is to show the evaluation results of CBR classification and make a comparative analysis with existing ANN classification method.
Publication type: Conference paper
Research centre: Swinburne University of Technology. Sarawak Campus. School of Engineering, Computing and Science
Source: Proceedings of the 2012 8th International Conference on Computing Technology and Information Management (ICCM 2012), Seoul, Korea, 24-26 April 2012, Vol. 1, pp. 527-531
Publication year: 2012
Keyword(s): Artificial Neural Network; Case Base Reasoning; Classification; Mammogram mass detection; Mammograms; Segmentation
Publisher: IEEE
ISBN: 9788988678671, 8988678672
Publisher URL: http://ieeexplore.ieee.org/
Peer reviewed