Computational Intelligence and Healthcare Informatics. Группа авторов

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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.

      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.

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