Classification of Pneumonia from Chest X-ray Image using Machine Learning Models

Authors

  • D. Sreenivasa Rao Research Scholar, Department of Computer Science and Engineering, Annamalai University, Chidambaram, India. (Assistant Professor, Department of CSE, Sreenidhi Institute of Science & Technology, Telangana).
  • S. Anu H. Nair Assistant Professor, Department of CSE, Annamalai University, Chidambaram, India (Deputed to WPT Chennai).
  • Thota Venkat Narayana Rao 3Professor,Department of CSE and HOD-CSE(IoT), Sreenidhi Institute of Science & Technology, Yamnampet, Ghatkesar R R Dist. Telangana.
  • K. P. Sanal Kumar Assistant Professor, P.G Department of Computer Science, R. V. Government Arts College, Chengalpattu, India

Keywords:

Pneumonia, Machine Learning, Classification, Traditional Learning, and X-ray images.

Abstract

Chest X-ray images are extremely difficult to interpret due to the fact that they are produced using a projection imaging modality. This is largely owing to the fact that anatomical structure and disease are closely intertwined. A large number of chest X-rays helps radiologists develop their knowledge and diagnostic abilities after they have mastered the principles of chest X-ray analysis.Droplets fill the lungs and make breathing difficult as a result of pericardial effusion caused by pneumonia. Pneumonia can be treated more effectively and with a higher chance of survival if caught early. Chest X-ray imaging is the most routinely used diagnostic technique for pneumonia. Examining chest X-rays, on the other hand, is a tough task with a high degree of subjectivity. In this study, we employed chest X-ray pictures to develop a computer-aided specialised diagnostic system capable of identifying pneumonia.Researchers have seen how machine learning algorithms can be used to tell if a chest X-ray shows signs of pneumonia. The most important thing about this study's conclusion is that it sorts out the different types of pneumonia. The combined Scale Invariant Fourier Transform (SIFT)and Local Binary Pattern (LBP)features are extracted from each training image and fed into machine learning models such as the Random Forest (RF), Artificial Neural Network (ANN) and Decision Tree (DT) model. After that, the classification model was created and tested on a set of test images. With an accuracy of 91.29%, RF was able to correctly classify all of the patients who had been diagnosed with pneumonia. Based on these results, we can say that the proposed method described in this research paper may help doctors figure out what's wrong with people with typical pneumonia.

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References

ZubairS.An Efficient Method to Predict Pneumonia from Chest X-Rays Using Deep Learning Approach. The Importance Of Health Informatics In Public Health During A Pandemic. 272 pp. 457 (2020)

WHO Pneumonia. World Health Organization. (2019), https://www.who.int/news-room/fact-sheets/detail/pneumonia

Neuman M., Lee E., Bixby S., Diperna S., Hellinger J., Markowitz R., et al. Variability in the interpretation of chest radiographs for the diagnosis of pneumonia in children. Journal Of Hospital Medicine. 7,294–298 (2012) https://doi.org/10.1002/jhm.955 PMID: 22009855

Williams G., Macaskill P., Kerr M., Fitzgerald D., Isaacs D., Codarini M., et al. Variability and accuracyin interpretation of consolidation on chest radiography for diagnosing pneumonia in children under 5years of age. Pediatric Pulmonology. 48, 1195–1200 (2013) https://doi.org/10.1002/ppul.22806 PMID:23997040

Kermany D., Zhang K. &Goldbaum M. Labeled Optical Coherence Tomography (OCT) and Chest XRay Images for Classification. (Mendeley,2018)

Lal S., Rehman S., Shah J., Meraj T., Rauf H., Damasˇevičius R., et al. Adversarial Attack and Defencethrough Adversarial Training and Feature Fusion for Diabetic Retinopathy Recognition. Sensors. 21,3922 (2021) https://doi.org/10.3390/s21113922 PMID: 34200216

Rauf H., Lali M., Khan M., Kadry S., Alolaiyan H., Razaq A., et al. Time series forecasting of COVID-19transmission in Asia Pacific countries using deep neural networks. Personal And UbiquitousComputing. pp. 1–18 (2021) https://doi.org/10.1007/s00779-020-01494-0 PMID: 33456433

Deng J., Dong W., Socher R., Li L., Li K. &Fei-Fei, L. Imagenet: A large-scale hierarchical image database. 2009 IEEE Conference On Computer Vision And Pattern Recognition. pp. 248-255 (2009)

Dalhoumi S., Dray G., Montmain J., Derosière, G. &Perrey S. An adaptive accuracy-weighted ensemble for inter-subjects classification in brain-computer interfacing. 2015 7th International IEEE/EMBSConference On Neural Engineering (NER). pp. 126-129 (2015)

Albahli S., Rauf H., Algosaibi A. &Balas V. AI-driven deep CNN approach for multi-label pathology classification using chest X-Rays. PeerJ Computer Science. 7 pp. e495 (2021) https://doi.org/10.7717/peerj-cs.495 PMID: 33977135

Rahman T., Chowdhury M., Khandakar A., Islam K., Islam K., Mahbub Z., et al. Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X-ray. Applied Sciences.10, 3233 (2020) https://doi.org/10.3390/app10093233

Liang G. &Zheng L. A transfer learning method with deep residual network for pediatric pneumoniadiagnosis. Computer Methods And Programs In Biomedicine. 187 pp. 104964 (2020) https://doi.org/ 10.1016/j.cmpb.2019.06.023 PMID: 31262537

Ibrahim A., Ozsoz M., Serte S., Al-Turjman F. &Yakoi P. Pneumonia classification using deep learningfrom chest X-ray images during COVID-19. Cognitive Computation. pp. 1–13 (2021) https://doi.org/10.1007/s12559-020-09787-5 PMID: 33425044

Rajpurkar P., Irvin J., Zhu K., Yang B., Mehta H., Duan T., et al. & Others Chexnet: Radiologist-levelpneumonia detection on chest x-rays with deep learning. ArXiv Preprint ArXiv:1711.05225. (2017)

Ghebreyesus T, Lancet SS-T, 2020 U. Scientists are sprinting to outpace the novel coronavirus. thelancet.com 22. Mahase E. Covid-19: Russia approves vaccine without large scale testing or published results. BMJ: British Med J (Online). 2020:13;370.

Beck BR, Shin B, Choi Y, Park S, Kang K. Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARSCoV-2) through a drug-target interaction deep learning model. ComputStructBiotechnol J. 2020.

Li Y, Zhang J, Wang N, Li H, Shi Y, Guo G, Liu K, Zeng H, Zou Q. Therapeutic drugs targeting 2019-nCoV main protease by highthroughput screening. BioRxiv. 2020.

Vaishya R, Javaid M, Khan IH, Haleem A. Artifcial Intelligence (AI) applications for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clin Res Rev. 2020.

Zeng T, Zhang Y, Li Z, Liu X, Qiu B. Predictions of 2019-ncov transmission ending via comprehensive methods. arXiv preprint arXiv: 2002.04945. 2020.

Hu Z, Ge Q, Jin L, Xiong M. Artifcial intelligence forecasting of covid-19 in china. arXiv preprint arXiv: 2002.07112. 2020.

LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.

Tan C, Sun F, Kong T, Zhang W, Yang C, Liu C. A survey on deep transfer learning. InInternational Conference on Artifcial Neural Networks 2018. pp. 270–279. Springer Cham.

You Y, Zhang Z, Hsieh CJ, Demmel J, Keutzer K. Imagenet training in minutes. InProceedings of the 47th International Conference on Parallel Processing. pp. 1–10. 2018.

Han X, Zhong Y, Cao L, Zhang L. Pre-trained alexnet architecture with pyramid pooling and supervision for high spatial resolution remote sensing image scene classifcation. Remote Sens. 2017;9(8):848.

Lippi G, Simundic AM, Plebani M. Potential preanalytical and analytical vulnerabilities in the laboratory diagnosis of coronavirus disease 2019 (COVID-19). Clinical Chemistry and Laboratory Medicine (CCLM). 2020;1(ahead-of-print).

Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, Qiu Y, Wang J, Liu Y, Wei Y, Yu T. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. The Lancet. 2020;395(10223):507–13.

Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, Ferrero E, Agapow PM, Zietz M, Hofman MM, Xie W. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface. 2018;15(141):20170387.

Kallianos K, Mongan J, Antani S, Henry T, Taylor A, Abuya J, Kohli M. How far have we come? Artifcial intelligence for chest radiograph interpretation. ClinRadiol. 2019;1;74(5):338–45.

Wang F, Casalino LP, Khullar D. Deep learning in medicine—promise, progress, and challenges. JAMA Intern Med. 2019;179(3):293–4.

Wang W, Xu Y, Gao R, Lu R, Han K, Wu G, Tan W. Detection of SARS-CoV-2 in diferent types of clinical specimens. Jama. 2020; 12:323(18):1843–4.

Shan F, Gao Y, Wang J, Shi W, Shi N, Han M, Xue Z, Shi Y. Lung infection quantifcation of covid-19 in CT images with deep learning. arXiv preprint arXiv: 2003.04655. 2020.

Yang W, Yan F. Patients with RT-PCR-confrmed COVID-19 and normal chest CT. Radiology. 2020;295(2): E3-.

Apostolopoulos ID, Mpesiana TA. Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. PhysEngSci Med. 2020;3:1.

Narin A, Kaya C, Pamuk Z. Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. arXiv preprint arXiv: 2003.10849. 2020.

Wang L, Wong A. COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images. arXiv preprint arXiv: 2003.09871. 2020

Albahli S., Rauf H., Arif M., Nafis M. &Algosaibi A. Identification of thoracic diseases by exploiting deepneural networks. Neural Networks. 5 pp. 6 (2021)

Chandra T. &Verma K. Pneumonia detection on chest X-Ray using machine learning paradigm. Proceedings Of 3rd International Conference On Computer Vision And Image Processing. pp. 21-33(2020)

Kuo K., Talley P., Huang C. & Cheng L. Predicting hospital-acquired pneumonia among schizophrenicpatients: a machine learning approach. BMC Medical Informatics And Decision Making. 19, 1–8 (2019)https://doi.org/10.1186/s12911-019-0792-1 PMID: 30866913

Yue H., Yu Q., Liu C., Huang Y., Jiang Z., Shao C., et al. & Others Machine learning-based CT radiomics method for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: a multicenter study. Annals Of Translational Medicine. 8 (2020) https://doi.org/10.21037/atm-20-3026 PMID: 32793703

Sharma H., Jain J., Bansal P. & Gupta S. Feature extraction and classification of chest x-ray imagesusing cnn to detect pneumonia. 2020 10th International Conference On Cloud Computing, Data Science & Engineering (Confluence). pp. 227-231 (2020)

Stephen O., Sain M., Maduh U. &Jeong D. An efficient deep learning approach to pneumonia classification in healthcare. Journal Of Healthcare Engineering. 2019 (2019) https://doi.org/10.1155/2019/4180949 PMID: 31049186

Zhang J., Xie Y., Pang G., Liao Z., Verjans J., Li W., et al. & Others Viral Pneumonia Screening onChest X-rays Using Confidence-Aware Anomaly Detection. IEEE Transactions On Medical Imaging.(2020)

Tuncer T., Ozyurt F., Dogan S. &Subasi A. A novel Covid-19 and pneumonia classification methodbased on F-transform. ChemometricsAnd Intelligent Laboratory Systems. 210 pp. 104256 (2021)https://doi.org/10.1016/j.chemolab.2021.104256 PMID: 33531722

Jaiswal A., Tiwari P., Kumar S., Gupta D., Khanna A. & Rodrigues J. Identifying pneumonia in chest Xrays: A deep learning approach. Measurement. 145 pp. 511–518 (2019) https://doi.org/10.1016/j.measurement.2019.05.076

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Published

18.10.2022

How to Cite

Rao, D. S., S. Anu H. Nair, Narayana Rao, T. V., & Kumar, K. P. S. (2022). Classification of Pneumonia from Chest X-ray Image using Machine Learning Models. International Journal of Intelligent Systems and Applications in Engineering, 10(1s), 399–408. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2307

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Research Article