An Intellectual Brain MRI Image Retrieval System to aid the Diagnosis of Brain Tumors
Chethan K1, Rekha Bhandarkar2
1Chethan K, Research Scholar, Department of ECE, NMAMIT, Nitte (Karnataka), India.
2Rekha Bhandarkar, Professor, Department of ECE, NMAMIT, Nitte (Karnataka), India.
Manuscript received on 18 October 2019 | Revised Manuscript received on 25 October 2019 | Manuscript Published on 02 November 2019 | PP: 3191-3197 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B14180982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1418.0982S1119
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Human brain being a complex organ, detecting abnormalities like Brain Tumor, Alzheimer, and Schizophrenia etc. are not an easy task. A computer aided automated system called Content Based Medical Image Retrieval System (CBMIR) can be used to assist the medical practitioner in arriving at correct diagnosis. Brain tumor is a kind of disease related to the brain malfunction and goes through various stages. Brain tumor identification, classification in its initial stages is an important and challenging task. In this paper, focus is on revival of brain tumor images from large database where multiple stages of diseases are present. In order to realize this task, a feature extraction technique comprising of Local Binary Pattern (LBP), Gabor, and Histogram of Gradient (HOG) are used. Based on the attributes, Support Vector Machine (SVM) classifier is used for pattern learning and classification. An experiment is performed to measure the accuracy of SVM classifier and performance of the CBMIR system for different classes of brain tumor disease. With the extracted attributes, SVM achieves an accuracy of 89.33%, average precision of 89.35% and recall of 89.33%.
Keywords: CBMIR; Brain Tumor, SVM Classifier, Feature Extraction, Euclidean Distance, Manhattan distance.
Scope of the Article: Image Security