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

Content Provider	IEEE Xplore Digital Library
Author	Raman, V. Then, P. Sumari, P.
Copyright Year	2012
Description	Author affiliation: School of Engineering Computing & Science, Swinburne University of Technology Sarawak, Kuching, Malaysia (Raman, V.; Then, P.) \|\| School of Computer Science, University Sains Malaysia, Penang, Malaysia (Sumari, P.)
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.
Starting Page	527
Ending Page	531
File Size	1252239
Page Count	5
File Format	PDF
ISBN	9781467308939
e-ISBN	9788988678688
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-04-24
Publisher Place	Korea (South)
Access Restriction	Subscribed
Rights Holder	AICIT
Subject Keyword	Training Case Base Reasoning and Aritificial Neural Network Segmentation Artificial neural networks Classification Educational institutions Cognition Entropy History Cancer
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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