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1. Abbas, A., Abdelsamea, M.M., Gaber, M.M., Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Applied Intelligence, arXiv preprint arXiv:2003.13815., 51, 2, 854–864 2020.

2. Abiyev, R.H. and Ma’aitah, M.K.S., Deep convolutional neural networks for chest diseases detection. J. Healthcare Eng., 2018, 1–11, 2018.

3. Apostolopoulos, I.D., Aznaouridis, S.I., Tzani, M.A., Extracting possibly representative COVID-19 Biomarkers from X-Ray images with Deep Learning approach and image data related to Pulmonary Diseases. J. Med. Biol. Eng., 1, 40, 462–469, 2020.

4. Bar, Y., Diamant, I., Wolf, L., Lieberman, S., Konen, E., Greenspan, H., Chest pathology detection using deep learning with non-medical training, in: 2015 IEEE 12th international symposium on biomedical imaging (ISBI), 2015, April, IEEE, pp. 294–297.

5. Behzadi-khormouji, H., Rostami, H., Salehi, S., Derakhshande-Rishehri, T., Masoumi, M., Salemi, S., Batouli, A., Deep learning, reusable and problem-based architectures for detection of consolidation on chest X-ray images. Comput. Methods Programs Biomed., 185, 105162, 2020.

6. Belarus tuberculosis portal. Available at: http://tuberculosis.by.

7. Bharati, S., Podder, P., Mondal, M.R.H., Hybrid deep learning for detecting lung diseases from X-ray images. Inf. Med. Unlocked, 20, 100391, 2020.

8. Bouslama, A., Laaziz, Y., Tali, A., Diagnosis and precise localization of cardiomegaly disease using U-NET. Inf. Med. Unlocked, 19, 100306, 2020.

9. Chauhan, A., Chauhan, D., Rout, C., Role of gist and PHOG features in computer-aided diagnosis of tuberculosis without segmentation. PLoS One, 9, 11, e112980, 2014.

10. Chen, B., Li, J., Guo, X., Lu, G., DualCheXNet: dual asymmetric feature learning for thoracic disease classification in chest X-rays. Biomed. Signal Process. Control, 53, 101554, 2019.

11. Cheng, J.Z., Ni, D., Chou, Y.H., Qin, J., Tiu, C.M., Chang, Y.C., Chen, C.M., Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci. Rep., 6, 1, 1–13, 2016.

12. Chollet, F., Xception: Deep learning with depthwise separable convolutions, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1251–1258, 2017.

13. Cicero, M., Bilbily, A., Colak, E., Dowdell, T., Gray, B., Perampaladas, K., Barfett, J., Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs. Invest. Radiol., 52, 5, 281–287, 2017.

14. Ciompi, F., de Hoop, B., van Riel, S.J., Chung, K., Scholten, E.T., Oudkerk, M., van Ginneken, B., Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box. Med. Image Anal., 26, 1, 195–202, 2015.

15. Demner-Fushman, D., Kohli, M.D., Rosenman, M.B., Shooshan, S.E., Rodriguez, L., Antani, S., McDonald, C.J., Preparing a collection of radiology examinations for distribution and retrieval. J. Am. Med. Inf. Assoc., 23, 2, 304–310, 2016.

16. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L., Imagenet: A large-scale hierarchical image database, in: 2009 IEEE conference on computer vision and pattern recognition, 2009, June, IEEE, pp. 248–255.

17. Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T., Decaf: A deep convolutional activation feature for generic visual recognition, in: International conference on machine learning, 2014, January, pp. 647–655.

18. Dunnmon, J.A., Yi, D., Langlotz, C.P., Ré, C., Rubin, D.L., Lungren, M.P., Assessment of convolutional neural networks for automated classification of chest radiographs. Radiology, 290, 2, 537–544, 2019.

19. Guan, Q., Huang, Y., Zhong, Z., Zheng, Z., Zheng, L., Yang, Y., Diagnose like a radiologist: Attention guided convolutional neural network for thorax disease classification. arXiv preprint arXiv:1801.09927, 1–10, 2018.

20. Guan, Q., Huang, Y., Zhong, Z., Zheng, Z., Zheng, L., Yang, Y., Diagnose like a radiologist: Attention guided convolutional neural network for thorax disease classification. Pattern Recognition Letters, arXiv preprintarXiv:1801.09927, 131, 38–45, 2018.

21. He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778, 2016.

22. He, K., Zhang, X., Ren, S., Sun, J., Identity mappings in deep residual networks, in: European conference on computer vision, 2016, October, Springer, Cham, pp. 630–645.

23. Ho, T.K.K. and Gwak, J., Multiple feature integration for classification of thoracic disease in chest radiography. Appl. Sci., 9, 19, 4130, 2019.

24. https://www.who.int/news-room/fact-sheets/detail/tuberculosis [accessed on 24 Nov. 2020]

25. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q., Densely connected convolutional networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700–4708, 2017.

26. Huang, Z., Lin, J., Xu, L., Wang, H., Bai, T., Pang, Y., Meen, T.H., Fusion High-Resolution Network for Diagnosing ChestX-ray Images. Electronics, 9, 1, 190, 2020.

27. Hwang, S., Kim, H.E., Jeong, J., Kim, H.J., A novel approach for tuberculosis screening based on deep convolutional neural networks, in: Medical imaging 2016: computer-aided diagnosis, vol. 9785, pp. 97852W, International Society for Optics and Photonics, 2016 March.

28. Islam, M.T., Aowal, M.A., Minhaz, A.T., Ashraf, K., Abnormality detection and localization in chest x-rays using deep convolutional neural networks. arXiv preprint arXiv:1705.09850, 1–16, 2017.

29. Jaeger, S., Candemir, S., Antani, S., Wáng, Y.X.J., Lu, P.X., Thoma, G., Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. Quant. Imaging Med. Surg., 4, 6, 475, 2014.

30. Jaeger, S., Karargyris, A., Candemir, S., Folio, L., Siegelman, J., Callaghan, F., Thoma, G., Automatic tuberculosis screening using chest radiographs. IEEE Trans. Med. Imaging, 33, 2, 233–245, 2013.

31. Jain, G., Mittal, D., Thakur, D., Mittal, M.K., A deep learning approach to detect Covid-19 coronavirus with X-Ray images. Biocybern. Biomed. Eng., 40, 4, 1391–1405, 2020.

32. Jain, R., Gupta, M., Taneja, S., Hemanth, D.J., Deep learning based detection and analysis of COVID-19 on chest X-ray images. Appl. Intell., 51, 3, 1690–1700, 2020.

33. Karargyris, A., Siegelman, J., Tzortzis, D., Jaeger, S., Candemir, S., Xue, Z., Thoma, G.R., Combination of texture and shape features to detect pulmonary abnormalities in digital chest X-rays. Int. J. Comput. Assist. Radiol. Surg., 11, 1, 99–106, 2016.

34. Krizhevsky, A., Sutskever, I., Hinton, G.E., Imagenet classification with deep convolutional neural networks. Commun. ACM, 60, 6, 84–90, 2017.

35. Lakhani, P. and Sundaram, B., Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology, 284, 2, 574–582, 2017.

36. Li, R., Zhang, W., Suk, H.I., Wang, L., Li, J., Shen, D., Ji, S., Deep learning based imaging data completion for improved brain disease diagnosis, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, 2014, September, Springer, Cham, pp. 305–312.

37. Li, Z., Wang, C., Han, M., Xue, Y., Wei, W., Li, L.J., Fei-Fei, L., Thoracic disease identification and localization with limited supervision, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8290–8299, 2018.

38. Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., Sánchez, C.I., A survey on deep learning in medical image analysis. Med. Image Anal., 42, 60–88, 2017.

39. Liu, W., Rabinovich, A., Berg, A.C., Parsenet: Looking wider to see better. arXiv preprint arXiv:1506.04579, Workshop track - ICLR 2016, 1–11, 2015.

40. Lopes, U.K. and Valiati, J.F., Pre-trained convolutional neural networks as feature extractors for tuberculosis detection. Comput. Biol. Med., 89, 135–143, 2017.

41. Ma, Y., Zhou, Q., Chen, X., Lu, H., Zhao, Y., Multi-attention network for thoracic disease classification and localization, in: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019, May, IEEE, pp. 1378–1382.

42. Melendez, J., Sánchez, C.I., Philipsen, R.H., Maduskar, P., Dawson, R., Theron, G., Van Ginneken, B., An automated tuberculosis screening strategy combining X-ray-based computer-aided detection and clinical information. Sci. Rep., 6, 25265, 2016.

43. Mukherjee, A., Feature Engineering for Cardio-Thoracic Disease Detection from NIH Chest Radiographs, in: Computational Intelligence in Pattern Recognition, pp. 277–284, Springer, Singapore, 2020.

44. Müller, R., Kornblith, S., Hinton, G.E., When does label smoothing help?, in: Advances in Neural Information Processing Systems, pp. 4694–4703, 2019.

45. Ozturk, T., Talo, M., Yildirim, E.A., Baloglu, U.B., Yildirim, O., Acharya, U.R., Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput. Biol. Med., 121, 103792, 2020.

46. Pasa, F., Golkov, V., Pfeiffer, F., Cremers, D., Pfeiffer, D., Efficient deep network architectures for fast chest X-ray tuberculosis screening and visualization. Sci. Rep., 9, 1, 1–9, 2019.

47. Pham, H.H., Le, T.T., Tran, D.Q., Ngo, D.T., Nguyen, H.Q., Interpreting chest X-rays via CNNs that exploit disease dependencies and uncertainty labels. medRxiv, 19013342, 1–27, 2019.

48. Qin, C., Yao, D., Shi, Y., Song, Z., Computer-aided detection in chest radiography based on artificial intelligence: a survey. Biomed. Eng. Online, 17, 1, 113, 2018.

49. Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Lungren, M.P., Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225, 05225, 1–6, 2017.

50. Roth, H.R., Lu, L., Liu, J., Yao, J., Seff, A., Cherry, K., Summers, R.M., Improving computer-aided detection using convolutional neural networks and random view aggregation. IEEE Trans. Med. Imaging, 35, 5, 1170–1181, 2015.

51. Roy, S., Menapace, W., Oei, S., Luijten, B., Fini, E., Saltori, C., Peschiera, E., Deep learning for classification and localization of COVID-19 markers in point-of-care lung ultrasound. IEEE Trans. Med. Imaging, 39, 8, 2676–2687, 2020.

52. Roy, S., Siarohin, A., Sangineto, E., Bulo, S.R., Sebe, N., Ricci, E., Unsupervised domain adaptation using feature-whitening and consensus loss, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9471–9480, 2019.

53. Rozenberg, E., Freedman, D., Bronstein, A., Localization with Limited Annotation for Chest X-rays, in: Machine Learning for Health Workshop, 2020, April, PMLR, pp. 52–65.

54. Ryoo, S. and Kim, H.J., Activities of the Korean institute of tuberculosis. Osong Public Health Res. Perspect., 5, S43–S49, 2014.

55. Sajjadi, M., Javanmardi, M., Tasdizen, T., Regularization with stochastic transformations and perturbations for deep semi-supervised learning. Adv. Neural Inf. Process. Syst., 29, 1163–1171, 2016.

56. Setio, A.A.A., Ciompi, F., Litjens, G., Gerke, P., Jacobs, C., Van Riel, S.J., van Ginneken, B., Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans. Med. Imaging, 35, 5, 1160–1169, 2016.

57. Shen, W., Zhou, M., Yang, F., Yang, C., Tian, J., Multi-scale convolutional neural networks for lung nodule classification, in: International Conference on Information Processing in Medical Imaging, 2015, June, Springer, Cham, pp. 588–599.

58. Shin, H.C., Roth, H.R., Gao, M., Lu, L., Xu, Z., Nogues, I., Summers, R.M., Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging, 35, 5, 1285–1298, 2016.

59. Shiraishi, J., Katsuragawa, S., Ikezoe, J., Matsumoto, T., Kobayashi, T., Komatsu, K.I., Doi, K., Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules. Am. J. Roentgenol., 174, 1, 71–74, 2000.

60. Simonyan, K. and Zisserman, A., Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, ICLR 2015, 1–14, 2014.

61. Sirazitdinov, I., Kholiavchenko, M., Mustafaev, T., Yixuan, Y., Kuleev, R., Ibragimov, B., Deep neural network ensemble for pneumonia localization from a large-scale chest x-ray database. Comput. Electr. Eng., 78, 388–399, 2019.

62. Soldati, G., Smargiassi, A., Inchingolo, R., Buonsenso, D., Perrone, T., Briganti, D.F., Tursi, F., Proposal for international standardization of the use of lung ultrasound for COVID-19 patients; a simple, quantitative, reproducible method. J. Ultrasound Med., 10, 39, 7, 1413–1419, 2020.

63. Suk, H.I., Lee, S.W., Shen, D., Alzheimer’s Disease Neuroimaging Initiative. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. NeuroImage, 101, 569–582, 2014.

64. Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A., Inception-v4, inception-resnet and the impact of residual connections on learning, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 31, No. 1, 2016.

65. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Rabinovich, A., Going deeper with convolutions, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1–9, 2015.

66. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z., Rethinking the inception architecture for computer vision, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818–2826, 2016.

67. Tang, Y.X., Tang, Y.B., Peng, Y., Yan, K., Bagheri, M., Redd, B.A., Summers, R.M., Automated abnormality classification of chest radiographs using deep convolutional neural networks. NPJ Digital Med., 3, 1, 1–8, 2020.

68. Vajda, S., Karargyris, A., Jaeger, S., Santosh, K.C., Candemir, S., Xue, Z., Thoma, G., Feature selection for automatic tuberculosis screening in frontal chest radiographs. J. Med. Syst., 42, 8, 146, 2018.

69. Wang, H. and Xia, Y., Chestnet: A deep neural network for classification of thoracic diseases on chest radiography. arXiv preprint arXiv:1807.03058, 1–8, 2018.

70. Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M., Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2097–2106, 2017.

71. Yao, L., Poblenz, E., Dagunts, D., Covington, B., Bernard, D., Lyman, K., Learning to diagnose from scratch by exploiting dependencies among labels. arXiv preprint arXiv:1710.10501, 1–12, 2017.

72. Zech, J.R., Badgeley, M.A., Liu, M., Costa, A.B., Titano, J.J., Oermann, E.K., Confounding variables can degrade generalization performance of radiological deep learning models. arXiv preprint arXiv:1807.00431, 1–15, 2018.

73. Zhang, R., Making convolutional networks shift-invariant again. arXiv preprint arXiv:1904. 11486, In International Conference on Machine Learning, pp. 7324–7334, PMLR, 1–11, 2019.

74. Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V., Learning transferable architectures for scalable image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8697–8710, 2018.

1 *Corresponding author: wajgi.rakhi@gmail.com

Computational Intelligence and Healthcare Informatics

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