Pneumonia Classification using SVM and GLCM, LBP and HOG Features

Abeer N Faisal

Authors

Abeer N Faisal Computer Information Systems Dep., College of Computer Science and Information Technology, University of Sumer, Thi-Qar, Rifai, Iraq

Keywords:

Pneumonia, SVM, GLCM, LBP, HOG

Abstract

A type of lung disease is Pneumonia that infect the respiratory system, and this disease can be recognized by X-ray images for the chest area. The presence of pneumonia or not need the medical professional to examine the X-ray image, and the rule of his decision not ignored, but may suffer from some problem that can led to mistake. Many studies introduced in this field by using new technology, artificial intelligent and deep-learning technics to reduce the error ratio. In this work we introduce a model that correctly classify the chest X-ray as a pneumonia or not, GLCM, LBP and HOG are used as a feature extraction methods after preprocess the used images. SVM used here as a classifier and the obtained results show the effectiveness and robustness SVM when used with a suitable preprocessing steps and prices feature extraction methods. The model Accuracy is 99.90% with Recall of 100%, Precision : 99.87 %, and F1-score: 99.94%. The used dataset is chest X-ray from kaggle with 5,216 chest X-ray images

References

Shahzad, M. A. Arshed, F. Liaquat, M. Tanveer, M. Hussain, and R. Alamdar, "Pneumonia classification from chest X-ray images using pre-trained network architectures," AWKUM Transactions on Computer Sciences, vol. 10, no. 2, pp. 34–44, Jul.–Dec. 2022.

T. Rajasenbagam, S. Jeyanthi, and J. A. Pandian, "Detection of pneumonia infection in lungs from chest X-ray images using deep convolutional neural network and content-based image retrieval techniques," Journal of Ambient Intelligence and Humanized Computing, 2021, doi: 10.1007/s12652-021-03075-2.

B. Q. Bui, T. T. T. Nguyen, D. M. Le, C. Tran, and C. Pham, "Innovative silicosis and pneumonia classification: Leveraging graph transformer post-hoc modeling and ensemble techniques," IEEE Journal of Biomedical and Health Informatics, arXiv:2501.00520 [cs.CV], Dec. 31, 2024.

A. Manickam, J. Jiang, Y. Zhou, A. Sagar, R. Soundrapandiyan, and R. D. J. Samuel, "Automated pneumonia detection on chest X-ray images: A deep learning approach with different optimizers and transfer learning architectures," Measurement, vol. 184, Nov. 2021, Art. no. 109953, doi: 10.1016/j.measurement.2021.109953.

R. Kundu, R. Das, Z. W. Geem, G. T. Han, and R. Sarkar, Pneumonia detection in chest X-ray images using an ensemble of deep learning models, PLoS One, vol. 16, no. 9 September, Sep. 202 1 , doi:10.1371/joumal.pone.0256630.

G. Williams et al., "Variability and accuracy in interpretation of consolidation on chest radiography for diagnosing pneumonia in children under 5 years of age," Pediatric Pulmonology, vol. 48, pp. 1195–1200, 2013, doi: 10.1002/ppul.22806.

X. Wang et al., "Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2017, pp. 3462–3471.

P. Rajpurkar et al., "Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists," PLoS Medicine, vol. 15, no. 11, p. e1002686, 2018.

A. K. Jaiswal et al., "Identifying pneumonia in chest X-rays: A deep learning approach," Measurement, vol. 145, pp. 511–518, 2019.

G. Liang and L. Zheng, "A transfer learning method with deep residual network for pediatric pneumonia diagnosis," Computer Methods and Programs in Biomedicine, vol. 187, p. 104964, 2020.

T. Tuncer, F. Ozyurt, S. Dogan, and A. Subasi, "A novel Covid-19 and pneumonia classification method based on F-transform," Chemometrics and Intelligent Laboratory Systems, vol. 210, Mar. 2021, Art. no. 104256.

J. Zhang et al., "Viral pneumonia screening on chest X-rays using confidence-aware anomaly detection," IEEE Transactions on Medical Imaging, vol. 40, no. 3, pp. 879–890, Mar. 2021, doi: 10.1109/TMI.2020.3040950.

I. T. Jolliffe, Principal Component Analysis, 2nd ed. New York, NY, USA: Springer, 2002.

C. M. Bishop, Pattern Recognition and Machine Learning. New York, NY, USA: Springer, 2006.

Y. Zhang, Z. Dong, L. Wu, and S. Wang, "A hybrid method for MRI brain image classification," Expert Systems with Applications, vol. 38, no. 8, pp. 10049–10053, 2011.

C. Bergmeir and J. M. Benítez, "On the use of cross-validation for time series predictor evaluation," Information Sciences, vol. 191, pp. 192–213, 2012.

N. Sharma, A. K. Ray, S. Sharma, and K. K. Shukla, "Automated medical image segmentation techniques," Journal of Medical Physics, vol. 35, no. 1, pp. 3–14, 2010.

Kaggle, "Chest X-ray pneumonia," [Online]. Available: https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia.

R. C. Gonzalez and R. E. Woods, Digital Image Processing, 4th ed. Upper Saddle River, NJ, USA: Pearson, 2018.

W. K. Pratt, Digital Image Processing: PIKS Scientific Inside, 4th ed. Hoboken, NJ, USA: Wiley-Interscience, 2007.

K. Zuiderveld, "Contrast limited adaptive histogram equalization," in Graphics Gems IV, P. S. Heckbert, Ed. San Diego, CA, USA: Academic Press, 1994, pp. 474–485.

S. M. Suryanarayana and A. N. J. Raj, "Texture-based medical image classification using GLCM and deep learning techniques," Biomedical Signal Processing and Control, vol. 83, p. 104642, 2023.

L. Alzubaidi et al., "Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions," Journal of Big Data, vol. 8, p. 53, 2021.

S. Yasmin, S. Sharmin, S. Akter, and M. Paul, "Local binary pattern-based feature extraction for lung disease classification using chest X-ray images," Computers in Biology and Medicine, vol. 153, p. 106552, 2023.

N. S. Alghamdi and G. Muhammad, "Automated detection of pneumonia in chest X-ray images using local binary patterns and deep learning," IEEE Access, vol. 10, pp. 25305–25315, 2022.

N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2005, pp. 886–893.

M. T. Islam, M. M. Rahman, and S. Mahmud, "Lung disease detection from chest X-ray images using HOG features with machine learning," Informatics in Medicine Unlocked, vol. 38, p. 101168, 2023.

R. A. Wati, H. Irsyad, and M. E. A. Rivan, "Klasifikasi pneumonia menggunakan metode support vector machine," Jurnal Algoritme, vol. 1, no. 1, pp. 21–32, 2020.

T. Rahman et al., "Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X-ray," Applied Sciences, vol. 10, no. 9, p. 3233, 2020.

S. Minaee et al., "Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning," Medical Image Analysis, vol. 65, p. 101794, 2020.

D. Kermany et al., "Identifying medical diagnoses and treatable diseases by image-based deep learning," Cell, vol. 172, no. 5, pp. 1122–1131, 2018.