International Journal of Advances in Science, Engineering and Technology(IJASEAT)
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current issues
Volume-7,Issue-3  ( Jul, 2019 )
Statistics report
Nov
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
  Journal Paper

Paper Title
The Detection of Abnormalities in Lumber Spine MRI Images with Computer Aided Diagnostic Systems

Abstract
In today’s world the lower back pain the most significant problem and amongst the most of the causes of lower back pain, disc herniation is the major one. In this study, we proposed a robust CAD system for the detection of the abnormalities in the lumbar spine MRI images. One of the major advantages of this study is that it does not require specific segmentation of the MRI images and gives accurate results. By using KNN and SVM classifiers we found that SVM classifiers the best classifiers for the detection of the lumbar herniated disc. Furthermore, we also test the impact of bulging disc on the accuracy by treating bulging disc as normal and herniated. We found that bulging disc is an initial stage of herniation and thus considering the bulging disc as herniated, we achieve 95% accuracy. Keywords- computer-aided diagnostics, lumbar herniated disc, bulging disc, intensity features, texture features, classification, KNN, SVM


Author - Mamona Mumtaz, Munir Ahmad

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| Published on 2019-05-18
   
   
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