Abstract
COVID-19 has spread throughout the world since 2019, and the epidemic has placed huge demands on the detection performance of COVID-19. A ParNet model is proposed in this paper which uses parameter transfer learning to initialize the training weights trained on ImageNet and then verifies its rationality from the theoretical aspect through four ways including cosine similarity, image average Hash, perceptual Hash, and difference Hash. Four ways measure image similarity from different angles. In this paper, the parallel channel and spatial attention mechanism is used to replace the channel attention mechanism, and the Swish activation function is used to replace the ReLU activation function to improve the performance of ParNet. This paper proposes ParNet to detect CT of Covid-19. Compared with the classic and the state-of-the-art models, ParNet has better performance. Source code is publicly available.
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References
Ning, W., et al.: Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning. Nat. Biomed. Eng. 4(12), 1–11 (2020)
Konar, D., et al.: Auto-diagnosis of COVID-19 using lung CT images with semi-supervised shallow learning network. IEEE Access 99, 1 (2021)
Ahuja, S., Panigrahi, B.K., Dey, N., Rajinikanth, V., Gandhi, T.K.: Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices. Appl. Intell. 51(1), 571–585 (2020). https://doi.org/10.1007/s10489-020-01826-w
Perumal, V., Narayanan, V., Rajasekar, S.J.S.: Detection of COVID-19 using CXR and CT images using transfer learning and haralick features. Appl. Intell. 51(1), 341–358 (2020). https://doi.org/10.1007/s10489-020-01831-z
Zhou, T., Lu, H., Yang, Z., Qiu, S., Huo, B., Dong, Y.: The ensemble deep learning model for novel covid-19 on CT images. Appl. Soft Comput. 98, 106885 (2021)
Shalbaf, A., Vafaeezadeh, M.: Automated detection of COVID-19 using ensemble of transfer learning with deep convolutional neural network based on CT scans. Int. J. Comput. Assist. Radiol. Surg. 16(1), 115–123 (2021)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Hu, J., Shen, L., Albanie, S., Sun, G., Vedaldi, A.: Gather-excite: Exploiting feature context in convolutional neural networks. arXiv preprint arXiv:1810.12348
Roy, A.G., Navab, N., Wachinger, C.: Concurrent spatial and channel ‘Squeeze & Excitation’ in fully convolutional networks. In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science, vol. 11070, pp. 421-429. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_48
Sibi, P., Jones, S.A., Siddarth, P.: Analysis of different activation functions using back propagation neural networks. J. Theor. Appl. Inf. Technol. 47(3), 1264–1268 (2013)
Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 315–323. JMLR Workshop and Conference Proceedings (2011)
Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics. JMLR Workshop and Conference Proceedings, pp. 315–323 (2011)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Howard, A., et al.: Searching for mobilenetv3. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1314–1324 (2019)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2009)
Nguyen, H.V., Bai, L.: Cosine similarity metric learning for face verification. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010. LNCS, vol. 6493, pp. 709–720. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19309-5_55
Jiaheng, H., Xiaowei, L., Benhui, C., Dengqi, Y.: A comparative study on image similarity algorithms based on hash. J. Dali Univ. 2(12), 32 (2017)
Weng, L., Preneel, B.: A secure perceptual hash algorithm for image content authentication. In: De Decker, B., Lapon, J., Naessens, V., Uhl, A. (eds.) Communications and Multimedia Security, pp. 108–121. Springer Berlin Heidelberg, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24712-5_9
Wang, X., Pang, K., Zhou, X., Zhou, Y., Li, L., Xue, J.: A visual model-based perceptual image hash for content authentication. IEEE Trans. Inf. Forensics Secur. 10(7), 1336–1349 (2015)
Shao, L., Zhu, F., Li, X.: Transfer learning for visual categorization: a survey. IEEE Trans. Neural Netw. Learn. Syst. 26(5), 1019–1034 (2015)
Zhuang, F., et al.: A comprehensive survey on transfer learning. Proc. IEEE 109(1), 43–76 (2021)
Acknowledgments
We thank anonymous reviewers for valuable suggestions and comments. This work was supported by the National Natural Science Foundation of China (62062067), the Natural Science Foundation of Yunnan Province(2017FA032), and the Training Plan for Young and Middle-aged Academic Leaders of Yunnan Province (2018HB031).
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Zhao, C., Wang, S. (2021). Parameter Transfer Learning Measured by Image Similarity to Detect CT of COVID-19. In: Wei, Y., Li, M., Skums, P., Cai, Z. (eds) Bioinformatics Research and Applications. ISBRA 2021. Lecture Notes in Computer Science(), vol 13064. Springer, Cham. https://doi.org/10.1007/978-3-030-91415-8_23
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