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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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  • Cleveland Clinic. 2020. Spine Structure & Function: Parts & Segments, Spine Problems, Spine Health. [online] Available at: https://my.clevelandclinic.org/health/ articles/10040-spine-structure-andfunction [Accessed 5 August 2021].

  • Aghayev, K., 2019. Lumbar Disc Herniation. [online] Available at: [Accessed 5 August 2021].

  • Disabled World. 2021. Human Spine and Spinal Cord Picture C1 - S5 Vertebra. [online] Available at: [Accessed 5 August 2021].

  • Mayo Clinic. 2019. Herniated disk Symptoms and causes. [online] Available at: [Accessed 5 August 2021].

  • Dawson, E., n.d. Herniated Discs: Definition, Progression, and Diagnosis. [online] Available at: [Accessed 5 August 2021] 6. Alawneh, K., Al-dwiekat, M., Alsmirat, M. and Al-Ayyoub, M. 2015. In: . 6th International Conference on Information and Communication Systems (ICICS). Jordan, 7-9 April 2015. IEEE.

  • Beulah, A. and Sharmila, T. 2016. Classification of Intervertebral Disc on Lumbar MR Images using SVM. In: 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT). India, 21-23 July 2016. IEEE.

  • Alomari, R., Corso, J., Chaudhary, V. and Dhillon, G. 2010. Toward a clinical lumbar CAD: herniation diagnosis. International Journal of Computer Assisted Radiology and Surgery, 6, pp 119-126.

  • Ebrahimzadeh, E., Fayaz, F., Ahmadi, F. and Nikravan, M. 2018. A machine learning-based method in order to diagnose lumbar disc herniation disease by MR image processing. Biomedical Engineering: Applications Basis and Communications, 30(6), pp

  • Ghosh, S., Raja'S, A., Chaudhary, V., & Dhillon, G. (2011, March). Computer-aided diagnosis for lumbar mri using heterogeneous classifiers. In 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (pp. 1179-1182). IEEE.

  • Salehi, E., Yousefi, H., Rashidi, H., &Ghanaatti, H. (2019, April). Automatic Diagnosis of Disc Herniation in Two-Dimensional MR Images with Combination of Distinct Features Using Machine Learning Methods. In 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT) (pp. 1-6). IEEE.

  • Rehman, F., Shah, S. I. A., Riaz, N., & Gilani, S. O. (2019). A Robust Scheme of Vertebrae Segmentation for Medical Diagnosis. IEEE Access, 7, 120387-120398.

  • Mbarki W, Bouchouicha M, Frizzi S, Tshibasu F, Farhat LB, Sayadi M. Lumbar spine discs classification based on deep convolutional neural networks using axial view MRI. Interdisciplinary Neurosurgery. 2020 Dec 1;22:100837.

  • Shinde JV, Joshi YV, Manthalkar RR. Multidomain Feature Level Fusion for Classification of Lumbar Intervertebral Disc Using Spine MR Images. IETE Journal of Research. 2020 Jul 24:1-4 15. Bzdok, D., Krzywinski, M. and Altman, N., 2018. Machine Learning: Supervised methods, SVM and kNN. Nature Publishing Group, pp. 1-6.

  • Palaniappan, R., Sundaraj, K. and Sundaraj, S., 2014. A comparative study of the svm and k-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals. BMC Bioinformatics, 15 (223).

  • Nisar, H., Ch’ng, Y.K. and Ho, Y.K., 2020, November. Automatic Segmentation and Classification Of Eczema Skin Lesions Using Supervised Learning. In 2020 IEEE Conference on Open Systems (ICOS) (pp. 25-30). IEEE.

  • Nawaz, R., Cheah, K.H., Nisar, H. and Yap, V.V., 2020. Comparison of different feature extraction methods for EEG-based emotion recognition. Biocybernetics and Biomedical Engineering, 40(3), pp.910-926.

  • Cheah KH, Nisar H, Yap VV, Lee CY. Convolutional neuralnetworks for classification of music-listening EEG: comparing 1D convolutional kernels with 2D kernels and cerebral laterality of musical influence. Neural Computing and

  • Applications. 2020 Jul;32(13):8867-91.

  • Nisar H, Hoe TC, Nawaz R. Reducing Sensors in Mental Imagery Based Cognitive Task for Brain Computer Interface. In 2020 14th International Conference on Signal Processing and Communication Systems (ICSPCS) 2020 Dec 14 (pp. 1-10). IEEE.

  • Nisar H, Ch'ng YK, Chew TY, Yap VV, Yeap KH, Tang JJ. A color space study for skin lesion segmentation. In2013 IEEE International Conference on Circuits and Systems (ICCAS) 2013 Sep 18 (pp. 172-176). IEEE.

  • Hall, P., Park, U. and Samworth, J., 2008. Choice of neighbor order in nearest-nerighbor classification. Annals of Statistics, 36(5), pp. 2135-2152.


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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