Employing Texture Features of Chest X-Ray Images and Machine Learning in COVID-19 Detection and Classification

Hiam Alquran; Mohammad Alsleti; Roaa Alsharif; Isam Abu Qasmieh; Ali Mohammad Alqudah; Nor Hazlyna Binti Harun

doi:10.13164/mendel.2021.1.009

Hiam Alquran Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid, 21163, Jordan
Mohammad Alsleti The Institute of Biomedical Technology, King Hussein Medical Center, Royal Jordanian Medical Service, Amman, 11855, Jordan
Roaa Alsharif College of applied medical Sciences, Radiological Science Program, King Saud University, Jeddah, 21435, Saudi Arabia
Isam Abu Qasmieh Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid, 21163, Jordan
Ali Mohammad Alqudah Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid, 21163, Jordan
Nor Hazlyna Binti Harun Data Science Research Lab, School of Computing, Universiti Utara Malaysia, 06010 Sintok, Kedah, Malaysia

DOI: https://doi.org/10.13164/mendel.2021.1.009

Keywords: COVID-19, COVID-19 pandemic, Respiratory infection detection, Pneumonia, K-Nearest Neighbor, Support Vector Machine, Random Forest

Abstract

The novel coronavirus (nCoV-19) was first detected in December 2019. It had spread worldwide and was declared coronavirus disease (COVID-19) pandemic by March 2020. Patients presented with a wide range of symptoms affecting multiple organ systems predominantly the lungs. Severe cases required intensive care unit (ICU) admissions while there were asymptomatic cases as well. Although early detection of the COVID-19 virus by Real-time reverse transcription-polymerase chain reaction (RT-PCR) is effective, it is not efficient; as there can be false negatives, it is time consuming and expensive. To increase the accuracy of in-vivo detection, radiological image-based methods like a simple chest X-ray (CXR) can be utilized. This reduces the false negatives as compared to solely using the RT-PCR technique. This paper employs various image processing techniques besides extracted texture features from the radiological images and feeds them to different artificial intelligence (AI) scenarios to distinguish between normal, pneumonia, and COVID-19 cases. The best scenario is then adopted to build an automated system that can segment the chest region from the acquired image, enhance the segmented region then extract the texture features, and finally, classify it into one of the three classes. The best overall accuracy achieved is 93.1% by exploiting Ensemble classifier. Utilizing radiological data to conform to a machine learning format reduces the detection time and increase the chances of survival.

References

Abu-Qasmieh, I., and Al-quran, H. Unrestricted lr detection for biomedical applications using coarse-to-fine hierarchical approach. IETImage Processing 12, 9 (2018), 1639–1645.

Alqudah, A., and Alqudah, A. M. Sliding window based support vector machine system for classification of breast cancer using histopatho-logical microscopic images. IETE Journal of Research(2019), 1–9.

Alqudah, A. M. Ovarian cancer classification using serum proteomic profiling and wavelet features a comparison of machine learning and features selection algorithms. Journal of Clinical Engineering 44(2019), 165–173.

Alqudah, A. M. Towards classifying non-segmented heart sound records using instantaneous frequency based features. Journal of Medical Engineering & Technology 43, 7 (2019), 418–430.

Alqudah, A. M., Qazan, S., Alquran, H., Qasmieh, I. A., and Alqudah, A. Covid-19 detection from x-ray images using different artificial intelligence hybrid models.Jordan Journal of Electrical Engineering 6, 2 (2020), 168–178.

Alquran, H., Abu-Qasmieh, I., Khresat, S., Younes, A., and Almomani, S.Weight estimation for anesthetic administration using singular value decomposition and template matching for supine subject of different obesity levels. Health and Technology 8(2018), 265–269.

Alquran, H., et al. Ecg classification using higher order spectral estimation and deep learning techniques.Neural Network World (2019), 207–219.

Alquran, H., Shaheen, E., O’Connor, J. M., and Mahd, M. Enhancement of 3D modeling and classification of microcalcifications in breast computed tomography (BCT). In Medical Imaging 2014: Image Processing (2014), S. Ourselinand M. A. Styner, Eds., vol. 9034, International Society for Optics and Photonics, SPIE, pp. 799 –807.

Amyar, A., Modzelewski, R., Li, H., and Ruan, S.Multi-task deep learning based ct imaging analysis for covid-19 pneumonia: Classification and segmentation.Computers in Biology and Medicine 126(2020), 104037–104037.

Cohen, J. P., et al. Covid-19 image data collection: Prospective predictions are the future, 2020.

Dietterich, T. G.Ensemble methods in machine learning. In Proceedings of the First International Workshop on Multiple Classifier Systems (Berlin, Heidelberg, 2000), MCS ’00, Springer-Verlag, p. 1–15.

Gozes, O., et al. Rapid ai development cycle for the coronavirus (covid-19) pandemic: Initial results for automated detection & patient monitoring using deep learning ct image analysis, 2020.

Hamed, A. Image processing of corona virus using interferometry. Optics and Photonics Journal 06 (2016), 75–86.

He, D.-C., Wang, L., and Guibert, J. Texture feature extraction. Pattern Recognition Letters 6, 4 (1987), 269–273.

Hu, S., et al. Weakly supervised deep learning for covid-19 infection detection and classification from ct images. IEEE Access 8 (2020), 118869–118883.

Jin, Y. J., et al. A rapid advice guideline for the diagnosis and treatment of 2019 novel coronavirus (2019-ncov) infected pneumonia (standard version). Military Med Res 7 (2020), 4.

Kermany, D., Zhang, K., and Goldbaum,M. Labeled optical coherence tomography (oct) and chest x-ray images for classification, mendeley data [online], 2018. doi: 10.17632/rscbjbr9sj.2.

Li, L., et al. Using artificial intelligence to detect covid-19 and community-acquired pneumonia based on pulmonary ct: Evaluation of the diagnostic accuracy. Radiology 296, 2 (2020), E65–E71.

Mageshkumar, C., Thiyagarajan, R., Natarajan, S., Arulselvi, S., and Sainarayanan, G. Gabor features and lda based face recognition with ann classifier. 2011 International Conference on Emerging Trends in Electrical and Computer Technology (2011), 831–836.

Mohanaiah, P., Sathyanarayana, P., and GuruKumar, L. Image texture feature extraction using glcm approach. International journal of scientific and research publications 3 (2013), 1–5.

Mooney,P. Chest x-ray images (pneumonia) [online]. https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia.

Narin, A., Kaya, C., and Pamuk, Z. Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. Pattern Analysis and Applications (2021).

Rahimzadeh, M., and Attar, A. A modified deep convolutional neural network for detecting covid-19 and pneumonia from chest x-ray images based on the concatenation of xception andresnet50v2. Informatics in Medicine Unlocked 19(2020), 100360.

Sethy, P. K., and Behera, S. K. Detection of coronavirus disease (covid-19) based on deep features. Preprints (2020), 2020030300.

Song, Y., et al. Deep learning enables accurate diagnosis of novel coronavirus (covid-19) with ct images. IEEE/ACM Transactions on Computational Biology and Bioinformatics (2021), 1–1.

Stoecklin, S. B., et al. First cases of coronavirus disease 2019 (covid-19) in france: surveillance, investigations and control measures, january 2020. Eurosurveillance 25 (2020).

Wan, S., et al. Integrated local binary pattern texture features for classification of breast tissue imaged by optical coherence microscopy. Medical Image Analysis 38 (2017), 104–116.

Wang, S., et al. A deep learning algorithm using ct images to screen for corona virus disease (covid-19). European Radiology (2021), 1–9.

Zhang, D., Wong, A., Indrawan, M., and Lu, G. Content based image retrieval using gabor texture features. IEEE Transactions on Pattern Analysis and Machine Intelligence (2000), 13–15.

Zhao, Y., Jia, W., Hu, R.-X., and Min, H. Completed robust local binary pattern for texture classification. Neurocomputing 106 (2013), 68–76.