Abstract
The application of AI in general and Deep learning, in particular, is becoming increasingly popular in human life. AI has been able to replace people in many fields, with data already synthesized and stored by computers that will help AI become smarter. One of the areas where AI can be applied very well is the medical field, especially X-ray imaging. In this study, we propose a convolutional network architecture to classify chest X-ray images as well as apply explanatory methods to trained models to support disease diagnosis. The proposed method provides insight into medical imaging to support the diagnosis of Pneumonia.
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References
Xi, X., Chengcheng, Y., Jing, Q., Zhang, L., et al.: Imaging and clinical features of patients with 2019 novel coronavirus SARS-CoV-2. Eur. J. Nucl. Med. Mol. Imaging 47(5), 1275–1280 (2020). https://doi.org/10.1007/s002-04735-9
Jefferson, T., Jefferson, S.K.: Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks, April 2017
Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., et al.: CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison, 21 January 2019 (2019, submitted)
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1798–1828 (2013)
Song, Q., Zhao, L., Luo, X., Dou, X.: Using deep learning for classification of lung nodules on computed tomography images (2017). https://doi.org/10.1155/2017/8314740
Kingma, D.P., Ba, J.L.: Adam : a method for stochastic optimization (2014). arXiv:1412.6980v9
Nguyen, T.H.: Metagenome-based disease classification with deep learning and visualizations based on self-organizing maps. In: Dang, T.K., Küng, J., Takizawa, M., Bui, S.H. (eds.) FDSE 2019. LNCS, vol. 11814, pp. 307–319. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35653-8_20
Hai Nguyen, T., Prifti, E., Sokolovska, N., Zucker, J.: Disease prediction using synthetic image representations of metagenomic data and convolutional neural networks. In: 2019 IEEE-RIVF International Conference on Computing and Communication Technologies (RIVF), Danang, Vietnam, pp. 1–6 (2019). https://doi.org/10.1109/RIVF.2019.8713670
Ma, C., Xu, S., Yi, X., Li, L., Yu, C.: Research on image classification method based on DCNN. In: 2020 International Conference on Computer Engineering and Application (ICCEA), Guangzhou, China, pp. 873–876 (2020). https://doi.org/10.1109/ICCEA50009.2020.00192
Liu, J., Yang, S., Huang, H., Li, Z., Shi, G.: A deep feature manifold embedding method for hyperspectral image classification. Remote Sens. Lett. 11(7), 620–629 (2020). https://doi.org/10.1080/2150704X.2020.1746855
Zhou, F., Yu, T., Du, R., et al.: Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study (2020). Lancet 395(10229), 1054–1062 (2020)
Narkhede, S.: Understanding AUC-ROC Curve (2018)
Pham, H.H., Le, T.T., Ngo, D.T., Tran, D.Q., Nguyen, H.Q.: Interpreting chest X-rays via CNNs that exploit hierarchical disease dependencies and uncertainty labels (2019). arXiv:1911.06475
Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017). https://doi.org/10.1016/j.media.2017.07.005
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Huynh, H.X., Dang, S.H., Phan, C.A., Nguyen, H.T. (2021). A Convolutional Neural Network on X-Ray Images for Pneumonia Diagnosis. In: Vinh, P.C., Rakib, A. (eds) Context-Aware Systems and Applications, and Nature of Computation and Communication. ICCASA ICTCC 2020 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 343. Springer, Cham. https://doi.org/10.1007/978-3-030-67101-3_17
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DOI: https://doi.org/10.1007/978-3-030-67101-3_17
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