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Comparative Performance of ResNet50 and VGG16 in Lung Infection Detection

Năm XB 2024 Tạp chí / Hội thảo Lecture Notes in Networks and Systems Volume 1205 LNNS Đơn vị CNTT DOI / Link https://doi.org/10.1007/978-3-031-80943-9_78 ↗

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Tóm tắt

In recent years, deep learning has become increasingly applicable in different fields, especially considering the increasing amount of data available in the medical field. Medical data plays a crucial role in the field of artificial intelligence. With millions of...

Tài liệu tham khảo

[1] Rasheed, J., Ali Hameed, A., Djeddi, C., Jamil, A., Al-Turjman, F.: A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images. Interdisc. Sci. Comput. Life Sci. 13(1), 103–117 (2021)

[2] Wang, D., Mo, J., Zhou, G., Xu, L., Liu, Y.: An efficient mixture of deep and machine learning models for COVID-19 diagnosis in chest X-ray images. PLOS ONE 15(11), e0242535 (2020). Publisher: Public Library of Science

[3] Ahammed, K., Satu, M., Abedin, M., Rahaman, M., Islam, S.M.S.: Early Detection of Coronavirus Cases Using Chest X-ray Images Employing Machine Learning and Deep Learning Approaches (2020)

[4] Kit Yee, S.L., Keen Raymond, W.J.: Pneumonia diagnosis using chest X-ray images and machine learning. In: Proceedings of the 2020 10th International Conference on Biomedical Engineering and Technology, ICBET ’20, pp. 101–105, New York, NY, USA (2020). Association for Computing Machinery

[5] Kim, S., Rim, B., Choi, S., Lee, A., Min, S., Hong, M.: Deep learning in multi-class lung diseases’ classification on chest X-ray images. Diagnostics 12(4), 915 (2022). Number: 4 Publisher: Multidisciplinary Digital Publishing Institute

[6] Farhan, A.M.Q., Yang, S.: Automatic lung disease classification from the chest X-ray images using hybrid deep learning algorithm. Multimedia Tools Appl. 82(25), 38561–38587 (2023)

[7] Kabiraj, A., Meena, T., Reddy, P.B., Roy, S.: Detection and classification of lung disease using deep learning architecture from X-ray images. In: Bebis, G., et al. (eds.) Advances in Visual Computing: 17th International Symposium, ISVC 2022, San Diego, CA, USA, October 3–5, 2022, Proceedings, Part I, pp. 444–455. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-20713-6_34

[8] Bharati, S., Podder, P., Mondal, M.R.H.: Hybrid deep learning for detecting lung diseases from X-ray images. Inform. Med. Unlocked 20, 100391 (2020)

[9] Multiple Lung Diseases Classification from chest X-ray images using deep learning approach. Int. J. Adv. Trends Comput. Sci. Eng. 10(5), 2936–2946 (2021)

[10] Chen, K.-C., et al.: Diagnosis of common pulmonary diseases in children by X-ray images and deep learning. Sci. Rep. 10(1), 17374 (2020). Publisher: Nature Publishing Group

[11] Shamrat, F.M.J.M., et al.: LungNet22: a fine-tuned model for multiclass classification and prediction of lung disease using X-ray images. J. Personalized Med. 12(5), 680 (2022). Number: 5 Publisher: Multidisciplinary Digital Publishing Institute

[12] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015). arXiv:1512.03385

[13] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2015). arXiv:1409.1556

[14] Kermany, D., Zhang, K., Goldbaum, M.: Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification (2018). Publisher: Mendeley Data

[15] Barber, D.: Bayesian Reasoning and Machine Learning, 1 Edn. Cambridge University Press (2012)

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