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So denoising of Magnetic Resonance (MR) images and making them easy for human understanding form is a challenge. This research work presents an efficient Hybrid Abnormal Detection Algorithm (HADA) to detect the abnormalities in any part of the human body by MRIs. The proposed technique includes five stages: Noise Reduction, Smoothing, Feature Extraction, Feature Reduction and Classification. The proposed algorithm has been implemented and Classification accuracy of 98.80% has been achieved. The result shows that the proposed technique is robust and effective compared to other recent works. The system developed using the proposed algorithm will be a good computer aided diagnosis and decision making system in healthcare.<\/jats:p>","DOI":"10.2478\/s13537-013-0107-z","type":"journal-article","created":{"date-parts":[[2013,9,24]],"date-time":"2013-09-24T14:07:24Z","timestamp":1380031644000},"source":"Crossref","is-referenced-by-count":3,"title":["Efficient computer aided diagnosis of abnormal parts detection in magnetic resonance images using hybrid abnormality detection algorithm"],"prefix":"10.2478","volume":"3","author":[{"given":"C.","family":"Lakshmi Devasena","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M.","family":"Hemalatha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","reference":[{"issue":"11","key":"107_CR1","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.17485\/ijst\/2011\/v4i11.35","volume":"4","author":"S N Deepa","year":"2011","unstructured":"S. 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