Enhancing multiclass pneumonia classification with Machine Learning and textural features

Main Article Content

A. Beena Godbin
S. Graceline Jasmine


Keywords : COVID-19, Chest X-ray, Feature Extraction, GLCM, GLRLM, Machine Learning, Random Forest, XGB, SVM
Abstract

The highly infectious and mutating COVID-19, known as the novel coronavirus, poses a substantial threat to both human health and the global economy. Detecting COVID-19 early presents a challenge due to its resemblance to pneumonia. However, distinguishing between the two is critical for saving lives. Chest X-rays, empowered by machine learning classifiers and ensembles, prove effective in identifying multiclass pneumonia in the lungs, leveraging textural characteristics such as GLCM and GLRLM. These textural features are instilled into the classifiers and ensembles within the domain of machine learning. This article explores the multiclass categorization of X-ray images across four categories: COVID-19-impacted, bacterial pneumonia-affected, viral pneumonia-affected, and normal lungs. The classification employs Random Forest, Support Vector Machine, K-Nearest Neighbor, LGBM, and XGBoost. Random Forest and LGBM achieve an impressive accuracy of 92.4% in identifying GLCM features. The network's performance is evaluated based on accuracy, precision, sensitivity and F1-score.

Article Details

How to Cite
Godbin, A. B., & Jasmine, S. G. (2023). Enhancing multiclass pneumonia classification with Machine Learning and textural features. Machine Graphics and Vision, 32(3/4), 83–106. https://doi.org/10.22630/MGV.2023.32.3.5
References

A. Abbas, M. M. Abdelsamea, and M. M. Gaber. Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Applied Intelligence, 51:854-864, 2021. https://doi.org/10.1007/s10489-020-01829-7. (Crossref)

A. Abbasian Ardakani, U. R. Acharya, S. Habibollahi, and A. Mohammadi. COVIDiag: a clinical CAD system to diagnose COVID-19 pneumonia based on CT findings. European Radiology, 31:121-130, 2021. https://doi.org/10.1007/s00330-020-07087-y. (Crossref)

P. Afshar, S. Heidarian, F. Naderkhani, A. Oikonomou, K. N. Plataniotis, et al. COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images. Pattern Recognition Letters, 138:638-643, 2020. https://doi.org/10.1016/j.patrec.2020.09.010. (Crossref)

S. Ahuja, B. K. Panigrahi, N. Dey, V. Rajinikanth, and T. K. Gandhi. Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices. Applied Intelligence, 51:571-585, 2021. https://doi.org/10.1007/s10489-020-01826-w. (Crossref)

D. Al-Karawi, S. Al-Zaidi, N. Polus, and S. Jassim. Machine learning analysis of chest CT scan images as a complementary digital test of coronavirus (COVID-19) patients. MedRxiv, 2020. MedRxiv.2020.04.13.20063479. https://doi.org/10.1101/2020.04.13.20063479. (Crossref)

N. Alsharman and I. Jawarneh. GoogleNet CNN neural network towards chest CT-coronavirus medical image classification. Journal of Computer Science, 16(5):620-625, 2020. https://doi.org/10.3844/jcssp.2020.620.625. (Crossref)

I. D. Apostolopoulos, S. I. Aznaouridis, and M. A. Tzani. Extracting possibly representative COVID-19 biomarkers from X-ray images with deep learning approach and image data related to pulmonary diseases. Journal of Medical and Biological Engineering, 40:462-469, 2020. https://doi.org/10.1007/s40846-020-00529-4. (Crossref)

M. Barstugan, U. Ozkaya, and S. Ozturk. Coronavirus (COVID-19) classification using CT images by machine learning methods. arXiv, 2020. ArXiv:2003.09424. https://doi.org/10.48550/arXiv.2003.09424.

B. E. Boser, I. M. Guyon, and V. N. Vapnik. A training algorithm for optimal margin classifiers. In: Proc. 5th Annual Workshop on Computational Learning Theory, COLT '92, pp. 144-152. Association for Computing Machinery, 1992. https://doi.org/10.1145/130385.130401. (Crossref)

L. Breiman. Random forests. Machine Learning, 45(1):5-32, 2001. https://doi.org/10.1023/A:1010933404324. (Crossref)

L. Brunese, F. Mercaldo, A. Reginelli, and A. Santone. Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays. Computer Methods and Programs in Biomedicine, 196:105608, 2020. https://doi.org/10.1016/j.cmpb.2020.105608. (Crossref)

T. Chen and C. Guestrin. XGBoost: A scalable tree boosting system. In: Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '16, pp. 785-794. Association for Computing Machinery, San Francisco, California, USA, 2016. https://doi.org/10.1145/2939672.2939785. (Crossref)

M. E. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M. A. Kadir, et al. Can AI help in screening viral and COVID-19 pneumonia? IEEE Access, 8:132665-132676, 2020. https://doi.org/10.1109/ACCESS.2020.3010287. (Crossref)

N. Dey, V. Rajinikanth, S. J. Fong, M. S. Kaiser, and M. Mahmud. Social group optimization-assisted kapur’s entropy and morphological segmentation for automated detection of COVID-19 infection from computed tomography images. Cognitive Computation, 12:1011-1023, 2020. https://doi.org/10.1007/s12559-020-09751-3. (Crossref)

R. O. Duda, P. E. Hart, et al. Pattern Classification and Scene Analysis, vol. 3. Wiley New York, 1973.

K. El Asnaoui and Y. Chawki. Using X-ray images and deep learning for automated detection of coronavirus disease. Journal of Biomolecular Structure and Dynamics, 39(10):3615-3626, 2021. https://doi.org/10.1080/07391102.2020.1767212. (Crossref)

K. El Asnaoui, Y. Chawki, and A. Idri. Automated methods for detection and classification pneumonia based on X-ray images using deep learning. In: Artificial Intelligence and Blockchain for Future Cybersecurity Applications, pp. 257-284. Springer, 2021. https://doi.org/110.1007/978-3-030-74575-2_14. (Crossref)

R. M. Haralick, K. Shanmugam, and I. Dinstein. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(6):610-621, 1973. https://doi.org/10.1109/TSMC.1973.4309314. (Crossref)

C. Jin, W. Chen, Y. Cao, Z. Xu, Z. Tan, et al. Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nature Communications, 11(1):5088, 2020. https://doi.org/10.1038/s41467-020-18685-1. (Crossref)

S. H. Kassania, P. H. Kassanib, M. J. Wesolowskic, K. A. Schneidera, and R. Detersa. Automatic detection of coronavirus disease (COVID-19) in X-ray and CT images: A machine learning based approach. Biocybernetics and Biomedical Engineering, 41(3):867-879, 2021. https://doi.org/10.1016/j.bbe.2021.05.013. (Crossref)

G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, et al. LightGBM: A highly efficient gradient boosting decision tree. In: I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, et al., eds., Advances in Neural Information Processing Systems - Proc. NIPS 2017, vol. 30. Curran Associates, Inc., 2017. https://proceedings.neurips.cc/paper_files/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html.

C. Liu, X. Wang, C. Liu, Q. Sun, and W. Peng. Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning. Biomedical Engineering Online, 19(1):1-14, 2020. https://doi.org/10.1186/s12938-020-00809-9. (Crossref)

H. Mohammad-Rahimi, M. Nadimi, A. Ghalyanchi-Langeroudi, M. Taheri, and S. Ghafouri-Fard. Application of machine learning in diagnosis of COVID-19 through X-ray and CT images: A scoping review. Frontiers in Cardiovascular Medicine, 8:638011, 2021. https://doi.org/10.3389/fcvm.2021.638011. (Crossref)

P. Mooney. Chest X-Ray Images (pneumonia) Dataset, 2018. https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia, Kaggle dataset.

Y. Oh, S. Park, and J. C. Ye. Deep learning COVID-19 features on CXR using limited training data sets. IEEE Transactions on Medical Imaging, 39(8):2688-2700, 2020. https://doi.org/10.1109/TMI.2020.2993291. (Crossref)

T. Ojala, M. Pietikäinen, and D. Harwood. A comparative study of texture measures with classification based on featured distributions. Pattern Recognition, 29(1):51-59, 1996. https://doi.org/10.1016/0031-32032895.2900067-4. (Crossref)

K. Preetha and Dr. S. K. Jayanthi. GLCM and GLRLM based feature extraction technique in mammogram images. International Journal of Engineering and Technology, 7:266, 2018. https://doi.org/10.14419/IJET.V7I2.21.12378. (Crossref)

M. Rahimzadeh and A. Attar. A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2. Informatics in Medicine Unlocked, 19:100360, 2020. https://doi.org/10.1016/j.imu.2020.100360. (Crossref)

S. Rajaraman and S. Antani. Weakly labeled data augmentation for deep learning: a study on COVID-19 detection in chest X-rays. Diagnostics, 10(6):358, 2020. https://doi.org/10.3390/diagnostics10060358. (Crossref)

F. Shi, L. Xia, F. Shan, B. Song, D. Wu, et al. Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification. Physics in Medicine & Biology, 66(6):065031, 2021. https://doi.org/10.1088/1361-6560/abe838. (Crossref)

Y. Song, S. Zheng, L. Li, X. Zhang, X. Zhang, et al. Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(6):2775-2780, 2021. https://doi.org/10.11092FTCBB.2021.3065361. (Crossref)

A. Tahamtan and A. Ardebili. Real-time rt-pcr in covid-19 detection: issues affecting the results. Expert Review of Molecular Diagnostics, 20(5):453-454, 2020. https://doi.org/10.1080/14737159.2020.1757437. (Crossref)

R. Tawsifur, C. D. Muhammad, and K. Amith. COVID-19 radiography database, 2019. https://doi.org/https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database, Kaggle datset.

N. Tsiknakis, E. Trivizakis, E. E. Vassalou, G. Z. Papadakis, D. A. Spandidos, et al. Interpretable artificial intelligence framework for COVID-19 screening on chest X-rays. Experimental and Therapeutic Medicine, 20(2):727-735, 2020. https://doi.org/10.3892/etm.2020.8797. (Crossref)

S. Wang, B. Kang, J. Ma, X. Zeng, M. Xiao, et al. A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). European Radiology, 31:6096-6104, 2021. https://doi.org/10.10072Fs00330-021-07715-1. (Crossref)

M. Xu, D. Wang, H. Wang, X. Zhang, T. Liang, et al. COVID-19 diagnostic testing: Technology perspective. Clinical and Translational Medicine, 10(4):e158, 2020. https://doi.org/10.1002/ctm2.158. (Crossref)

X. Xu, X. Jiang, C. Ma, P. Du, X. Li, et al. A deep learning system to screen novel coronavirus disease 2019 pneumonia. Engineering, 6(10):1122-1129, 2020. https://doi.org/10.1016/j.eng.2020.04.010. (Crossref)

K. Zhang, X. Liu, J. Shen, Z. Li, Y. Sang, et al. Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography. Cell, 181(6):1423-1433, 2020. https://doi.org/10.1016/j.cell.2020.04.045. (Crossref)

C. Zheng, X. Deng, Q. Fu, Q. Zhou, J. Feng, et al. Deep learning-based detection for COVID-19 from chest CT using weak label. MedRxiv, 2020. MedRxiv.2020.03.12.20027185. https://doi.org/10.1101/2020.03.12.20027185. (Crossref)

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