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Classification of Lumber Spine Disc Herniation using Machine Learning Methods


Affiliations
1 Department of Electronic Engineering, Universititunku Abdul Rahman, 31900 Kampar, Malaysia
 

In the medical field computer-aided diagnosis systems (CADs) are an active area of research as CADs serve to aid medical professionals in simplifying the diagnosis of a patients condition. In this paper we propose a machine learning based method for classifying lumbar disc herniation. The automation of herniated disc diagnosis decreases the enormous weight on radiologists who need to analyse several cases every day manually. Automation will also help to decrease inter and intrarater variability. Hence his work focuses on the classification of lumber disc herniation based on sagittal view Magnetic Resonance Images (MRIs). The dataset used in this work comprises of 32 images from 32 patients of which 10 patients are healthy while 22 of them have herniated discs. This data is processed through various image processing techniques to obtain three sets of features: the binary image; shape, height and width measurements of discs; and full attribute images. The proposed approach consists of four stages: region extraction, image segmentation, feature extraction and classification. The classification process is performed through support vector machines (SVMs) and K-nearest neighbor (KNNs) of which the KNN with k=5 produced the best results with 78.6% accuracy, F1 score of 66.7%, precision and recall rate of 60% and 75% respectively.

Keywords

Classification; Image Processing; Lumbar Disc Herniation; Mri; Machine Learnin;, Segmentation.
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  • Classification of Lumber Spine Disc Herniation using Machine Learning Methods

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Authors

Tan Xin Hui Nicole
Department of Electronic Engineering, Universititunku Abdul Rahman, 31900 Kampar, Malaysia
Humaira Nisar
Department of Electronic Engineering, Universititunku Abdul Rahman, 31900 Kampar, Malaysia
Sim Kar Wei
Department of Electronic Engineering, Universititunku Abdul Rahman, 31900 Kampar, Malaysia

Abstract


In the medical field computer-aided diagnosis systems (CADs) are an active area of research as CADs serve to aid medical professionals in simplifying the diagnosis of a patients condition. In this paper we propose a machine learning based method for classifying lumbar disc herniation. The automation of herniated disc diagnosis decreases the enormous weight on radiologists who need to analyse several cases every day manually. Automation will also help to decrease inter and intrarater variability. Hence his work focuses on the classification of lumber disc herniation based on sagittal view Magnetic Resonance Images (MRIs). The dataset used in this work comprises of 32 images from 32 patients of which 10 patients are healthy while 22 of them have herniated discs. This data is processed through various image processing techniques to obtain three sets of features: the binary image; shape, height and width measurements of discs; and full attribute images. The proposed approach consists of four stages: region extraction, image segmentation, feature extraction and classification. The classification process is performed through support vector machines (SVMs) and K-nearest neighbor (KNNs) of which the KNN with k=5 produced the best results with 78.6% accuracy, F1 score of 66.7%, precision and recall rate of 60% and 75% respectively.

Keywords


Classification; Image Processing; Lumbar Disc Herniation; Mri; Machine Learnin;, Segmentation.

References





DOI: https://doi.org/10.13005/ojcst14.010203.01