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MRI Image Analysis based on Reverse Classification Accuracy Method

Varsha E. Jaware1 , Rajesh H. Kulkarni2

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-7 , Page no. 1309-1314, Jul-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i7.13091314

Online published on Jul 31, 2018

Copyright © Varsha E. Jaware, Rajesh H. Kulkarni . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Varsha E. Jaware, Rajesh H. Kulkarni, “MRI Image Analysis based on Reverse Classification Accuracy Method,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1309-1314, 2018.

MLA Style Citation: Varsha E. Jaware, Rajesh H. Kulkarni "MRI Image Analysis based on Reverse Classification Accuracy Method." International Journal of Computer Sciences and Engineering 6.7 (2018): 1309-1314.

APA Style Citation: Varsha E. Jaware, Rajesh H. Kulkarni, (2018). MRI Image Analysis based on Reverse Classification Accuracy Method. International Journal of Computer Sciences and Engineering, 6(7), 1309-1314.

BibTex Style Citation:
@article{Jaware_2018,
author = {Varsha E. Jaware, Rajesh H. Kulkarni},
title = {MRI Image Analysis based on Reverse Classification Accuracy Method},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {7 2018},
volume = {6},
Issue = {7},
month = {7},
year = {2018},
issn = {2347-2693},
pages = {1309-1314},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2604},
doi = {https://doi.org/10.26438/ijcse/v6i7.13091314}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i7.13091314}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2604
TI - MRI Image Analysis based on Reverse Classification Accuracy Method
T2 - International Journal of Computer Sciences and Engineering
AU - Varsha E. Jaware, Rajesh H. Kulkarni
PY - 2018
DA - 2018/07/31
PB - IJCSE, Indore, INDIA
SP - 1309-1314
IS - 7
VL - 6
SN - 2347-2693
ER -

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Abstract

Magnetic Resonance Imaging (MRI) scan are the most widely used radiographic techniques in diagnosis, clinical studies and treatment planning. It is important to be able to detect when an automatic segmentation method fails to avoid inclusion of wrong measurements into subsequent analysis which could otherwise lead to incorrect conclusion. Sometimes due to absence of Ground Truth (manually labelled) images it is difficult to detect the failure of automatic segmentation methods. Before deployment, performance is quantised using different metrics for which the predicted segmentation is compared to a reference segmentation also known as Ground Truth (GT), which is manually obtained by an expert. In some exceptional cases it becomes difficult to know about its real performance after deployment when a reference is unavailable. To that end, this paper aims to develop an improved and advanced technique of Reverse Classification Accuracy (RCA) on new data which enables us to discriminate between the successful and failed cases. The ideal concept is that to rank the ‘best’ segmentation results in the database without knowing the manual label. Then ‘match’ the rank between the prediction and the truth image saved in database. Further, for correctly and accurately segmented and classified brain MRI images, diseases are being detected using Random forest algorithm and Deep Learning.

Key-Words / Index Term

Machine learning, Deep Learning, image Segmentation, MRI images, classification, performance evaluation

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