New Image Texture Feature for Chest X-Ray Classification

Prajanto Wahyu Adi, Fajar Agung Nugroho, Yani Parti Astuti

Abstract


This study proposes a new feature extraction model to identify CXR images of covid-19 and pneumonia has a high visual resemblance. The feature extraction model starts by using histogram equalization and average filters as lowpass features and high pass features obtained through Laplacian and LoG filters. In the next step, covariance matrix of image along with the entire features are used to produce an eigen vector that will be used as a feature vector in the classification process. The final stage is the process of testing features on the classification algorithms KNN, SVM, LDA, Naïve Bayes, and Decision Tree through a 10-foldcross validation scheme with 0.9 training data and 0.1 test data. The first experiment for the Covid-19 and normal classes shows that the proposed model is able to produce an accuracy of 96% as the comparison model with GLCM texture extraction have an accuracy value of 91%. The second test is conducted for the class Covid-19 and pneumonia and obtained an accuracy value of 89% for the proposed model and 73% for the GLCM texture extraction. Experiments proved that the proposed model successfully outperformed the GLCM texture extraction model in all of classification algorithms used.

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References


K. Roosa et al., “Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020,” Infect. Dis. Model., vol. 5, pp. 256–263, 2020, doi: 10.1016/j.idm.2020.02.002.

W. M. Shaban, A. H. Rabie, A. I. Saleh, and M. A. Abo-Elsoud, “A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier,” Knowledge-Based Syst., vol. 205, p. 106270, 2020, doi: 10.1016/j.knosys.2020.106270.

V. K. Gupta, A. Gupta, D. Kumar, and A. Sardana, “Prediction of COVID-19 confirmed, death, and cured cases in India using random forest model,” Big Data Min. Anal., vol. 4, no. 2, pp. 116–123, 2021, doi: 10.26599/BDMA.2020.9020016.

T. Yan, P. K. Wong, H. Ren, H. Wang, J. Wang, and Y. Li, “Automatic distinction between COVID-19 and common pneumonia using multi-scale convolutional neural network on chest CT scans,” Chaos, Solitons and Fractals, vol. 140, p. 110153, 2020, doi: 10.1016/j.chaos.2020.110153.

L. Sun et al., “Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT,” IEEE J. Biomed. Heal. INFORMATICS, vol. 24, no. 10, pp. 2798–2805, 2020.

J. Zhang, Y. Chu, and N. Zhao, “Supervised framework for COVID-19 classification and lesion localization from chest CT,” Ethiop. J. Heal. Dev., vol. 34, no. 4, pp. 235–242, 2020.

R. C. Joshi et al., “A deep learning-based COVID-19 automatic diagnostic framework using chest X-ray images,” Biocybern. Biomed. Eng., vol. 41, no. 1, pp. 239–254, 2021, doi: 10.1016/j.bbe.2021.01.002.

Z. Wang et al., “Automatically discriminating and localizing COVID-19 from community-acquired pneumonia on chest X-rays,” Pattern Recognit., vol. 110, 2021, doi: 10.1016/j.patcog.2020.107613.

J. D. Arias-Londono, J. A. Gomez-Garcia, L. Moro-Velazquez, and J. I. Godino-Llorente, “Artificial Intelligence applied to chest X-Ray images for the automatic detection of COVID-19. A thoughtful evaluation approach,” IEEE Access, vol. 8, pp. 226811–226827, 2020, doi: 10.1109/ACCESS.2020.3044858.

P. Torkian, H. Rajebi, T. Zamani, N. Ramezani, P. Kiani, and S. Akhlaghpoor, “Magnetic resonance imaging features of coronavirus disease 2019 (COVID-19) pneumonia: The first preliminary case series,” Clin. Imaging, vol. 69, no. 5, pp. 261–265, 2021, doi: 10.1016/j.clinimag.2020.09.002.

A. Gupta, Anjum, S. Gupta, and R. Katarya, “InstaCovNet-19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray,” Appl. Soft Comput., vol. 99, p. 106859, 2021, doi: 10.1016/j.asoc.2020.106859.

E. Hussain, M. Hasan, M. A. Rahman, I. Lee, T. Tamanna, and M. Z. Parvez, “CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images,” Chaos, Solitons and Fractals, vol. 142, p. 110495, 2021, doi: 10.1016/j.chaos.2020.110495.

E. F. Ohata et al., “Automatic detection of COVID-19 infection using chest X-ray images through transfer learning,” IEEE/CAA J. Autom. Sin., vol. 8, no. 1, pp. 239–248, 2021, doi: 10.1109/JAS.2020.1003393.

D. Yousri, M. Abd Elaziz, L. Abualigah, D. Oliva, M. A. A. Al-qaness, and A. A. Ewees, “COVID-19 X-ray images classification based on enhanced fractional-order cuckoo search optimizer using heavy-tailed distributions,” Appl. Soft Comput., vol. 101, p. 107052, 2021, doi: 10.1016/j.asoc.2020.107052.

Y. H. Wu et al., “JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation,” IEEE Trans. Image Process., vol. 30, pp. 3113–3126, 2021, doi: 10.1109/TIP.2021.3058783.

L. Wang et al., “Multi-classifier-based identification of COVID-19 from chest computed tomography using generalizable and interpretable radiomics features,” Eur. J. Radiol., vol. 136, p. 109552, 2021, doi: 10.1016/j.ejrad.2021.109552.

A. Sayg?l?, “A new approach for computer-aided detection of coronavirus (COVID-19) from CT and X-ray images using machine learning methods,” Appl. Soft Comput., vol. 105, p. 107323, 2021, doi: 10.1016/j.asoc.2021.107323.

S. Varela-Santos and P. Melin, “A new approach for classifying coronavirus COVID-19 based on its manifestation on chest X-rays using texture features and neural networks,” Inf. Sci. (Ny)., vol. 545, pp. 403–414, 2021, doi: 10.1016/j.ins.2020.09.041.

Y. Peng, Y. Tang, S. Lee, Y. Zhu, R. M. Summers, and Z. Lu, “COVID-19-CT-CXR: A freely accessible and weakly labeled chest X-Ray and CT image collection on COVID-19 from biomedical literature,” IEEE Trans. Big Data, vol. 7, no. 1, pp. 3–12, 2021, doi: 10.1109/TBDATA.2020.3035935.

B. Hochhegger et al., “Coronavirus Disease 2019 (COVID-19) Pneumonia Presentations in Chest Computed Tomography: A Pictorial Review,” Curr. Probl. Diagn. Radiol., vol. 50, no. 3, pp. 436–442, 2021, doi: 10.1067/j.cpradiol.2020.06.010.

H. X. Bai et al., “Performance of Radiologists in Differentiating COVID-19 from Non-COVID-19 Viral Pneumonia at Chest CT,” Radiology, vol. 296, no. 2, pp. E46–E54, 2020, doi: 10.1148/radiol.2020200823.

F. Xiong et al., “The clinical classification of patients with COVID-19 pneumonia was predicted by Radiomics using chest CT,” Medicine (Baltimore)., vol. 100, no. 12, pp. 1–8, 2021, doi: 10.1097/MD.0000000000025307.

T. Tuncer, F. Ozyurt, S. Dogan, and A. Subasi, “A novel Covid-19 and pneumonia classification method based on F-transform,” Chemom. Intell. Lab. Syst., vol. 210, no. January, p. 104256, 2021, doi: 10.1016/j.chemolab.2021.104256.

M. E. H. Chowdhury et al., “Can AI help in screening viral and COVID-19 pneumonia?,” IEEE Access, vol. 8, pp. 132665–132676, 2020.




DOI: https://doi.org/10.33633/jais.v7i1.5340

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Journal of Applied Intelligent System (e-ISSN : 2502-9401, p-ISSN : 2503-0493) is published by Department of Informatics Universitas Dian Nuswantoro Semarang and IndoCEISS.

  

 

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