Abstract
COVID-19 transmission is still a huge hidden danger for elderly people with underlying diseases and immunocompromised infants and children, COVID-19 is a kind of viral pneumonia, and it is important to accurately and quickly distinguish COVID-19 from common viral pneumonia. Recently, the Red-Billed Blue Magpie Algorithm (RBMO), a high-performance optimiser, has achieved very significant performance in model parameter optimisation. In order to take advantage of the great potential of RBMO, we propose a new classification method for the task of classifying CT images of pneumonia, called RBMO-Att-Bi-LSTM. This method is the first time that RBMO has been applied to bi-directional long and short-term memory networks (Bi-LSTM), where RBMO can optimise the parameters such as the number of nodes in the hidden layer of the model, the regularisation coefficients, and the learning rate, and at the same time, it uses the self-attentive mechanism to capture the dependencies at different locations in the input sequence. Compared with other state-of-the-art methods, the experimental results show that RBMO-Att-Bi-LSTM has the best performance in classifying CT images of pneumonia and proves the effectiveness of RBMO-Att-Bi-LSTM.
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References
Bratt, A., Williams, J.M., Liu, G., Panda, A., Patel, P.P., Walkoff, L., Sykes, A.M.G., Tandon, Y.K., Francois, C.J., Blezek, D.J., et al.: Predicting usual interstitial pneumonia histopathology from chest ct imaging with deep learning. Chest 162(4), 815–823 (2022)
Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. pp. 785–794 (2016)
Désir, C., Petitjean, C., Heutte, L., Salaun, M., Thiberville, L.: Classification of endomicroscopic images of the lung based on random subwindows and extra-trees. IEEE Trans. Biomed. Eng. 59(9), 2677–2683 (2012)
Fu, S., Li, K., Huang, H., Ma, C., Fan, Q., Zhu, Y.: Red-billed blue magpie optimizer: a novel metaheuristic algorithm for 2d/3d uav path planning and engineering design problems. Artif. Intell. Rev. 57(6), 1–89 (2024)
Hastie, T., Rosset, S., Zhu, J., Zou, H.: Multi-class adaboost. Statistics and its. Interface 2(3), 349–360 (2009)
Hosmer Jr, D.W., Lemeshow, S., Sturdivant, R.X.: Applied logistic regression. John Wiley & Sons (2013)
Hwaid, A.H., Zaidi, A.R.S., Salman, A.D.: Possible vertical transmission of sars-cov-2 during the third trimester of pregnancy, a mini-review. Am. J. Epidemiol. 10(1), 19–23 (2022)
Joachims, T.: Making large-scale svm learning. Practical Advances in Kernel Methods-Support Vector Learning (1999)
Kakarla, P., Vimala, C., Hemachandra, S.: An automatic multi-class lung disease classification using deep learning based bidirectional long short term memory with spiking neural network. Multimedia Tools and Applications pp. 1–29 (2023)
Karimzadeh Parizi, M., Keynia, F., Khatibi Bardsiri, A.: Woodpecker mating algorithm (wma): a nature-inspired algorithm for solving optimization problems. International Journal of Nonlinear Analysis and Applications 11(1), 137–157 (2020)
Karimzadeh Parizi, M., Keynia, F., et al.: Owma: An improved self-regulatory woodpecker mating algorithm using opposition-based learning and allocation of local memory for solving optimization problems. Journal of Intelligent & Fuzzy Systems 40(1), 919–946 (2021)
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., Liu, T.Y.: Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems 30 (2017)
Lindley, D.V.: Fiducial distributions and bayes’ theorem. Journal of the Royal Statistical Society. Series B (Methodological) pp. 102–107 (1958)
Mannepalli, D.P., Namdeo, V.: An effective detection of covid-19 using adaptive dual-stage horse herd bidirectional long short-term memory framework. Int. J. Imaging Syst. Technol. 32(4), 1049–1067 (2022)
Naseer, S., Khalid, S., Parveen, S., Abbass, K., Song, H., Achim, M.V.: Covid-19 outbreak: Impact on global economy. Front. Public Health 10, 1009393 (2023)
Olulana, K., Owolawi, P., Tu, C., Abe, B.: Long thorax disease classification using convolutional long short term memory. In: 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). pp. 1–6 (2021). 10.1109/ICECCME52200.2021.9591041
Petrovskaya, M.V., Chaplyuk, V.Z., Hossain, M.N., Abueva, M.M.S., Al Humssi, A.S.: The impact of covid-19 on global socio-economic spheres and international migration. In: Sustainable Development Risks and Risk Management: A Systemic View from the Positions of Economics and Law, pp. 119–123. Springer (2023)
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., Gulin, A.: Catboost: unbiased boosting with categorical features. Advances in neural information processing systems 31 (2018)
Rahman, S., Jabin, S., Haque, M.R., Lija, A.A., Ritu, H.F.: Diabetes prediction using machine learning algorithm
Sohrabi, S., Shu, F., Gupta, A., Sabbaghian, M.H., Mehrara Molan, A., Sajjadi, S.: Health impacts of covid-19 through the changes in mobility. Sustainability 15(5), 4095 (2023)
Zhang, S., Zheng, D., Hu, X., Yang, M.: Bidirectional long short-term memory networks for relation classification. In: Proceedings of the 29th Pacific Asia conference on language, information and computation. pp. 73–78 (2015)
Zhu, W., Xie, L., Han, J., Guo, X.: The application of deep learning in cancer prognosis prediction. Cancers 12(3), 603 (2020)
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Sun, Y. et al. (2025). RBMO-Att-Bi-LSTM: A Red-Billed Blue Magpie Optimiser-Self-attention Mechanism Based Optimisation of Bi-Directional Long- and Short-Term Memory Networks for Classification of COVID-19 CT Images. In: Sheng, Q.Z., et al. Advanced Data Mining and Applications. ADMA 2024. Lecture Notes in Computer Science(), vol 15390. Springer, Singapore. https://doi.org/10.1007/978-981-96-0840-9_12
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DOI: https://doi.org/10.1007/978-981-96-0840-9_12
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