Computational Intelligence and Healthcare Informatics

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.