Learning Rate Optimization of U-Net Architecture Using Grasshopper Optimization Algorithm to Enhance Accuracy in CT Image Segmentation of COVID-19 Patients

Subject Areas : Neural networks and deep learning

Alireza Mehravin ¹ , Mostafa Zaare ² , Reza Mortazavi ³

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Received: 2025-04-28 Accepted : 2025-06-11 Published : 2025-06-30

Keywords: Covid-19, GOA, Image segmentation, Metaheuristic, U-Net,

Abstract :

In light of its excellent learning accuracy and rate, rapid data processing, and independence from large databases for network training, the U-Net architecture is a well-known and popular deep learning architecture for image segmentation and feature extraction. Learning rate selection and updating are crucial in network training. As U-Net is a completely nonlinear network, classical mathematical optimization algorithms increase the probability of local optima. This analytical research paper used the grasshopper optimization algorithm (GOA) as a metaheuristic approach to optimize the learning rate of U-Net. The network was trained using 256*256 CT images of the lungs of COVID-19 infected and uninfected individuals. A total of 400 CT images were employed as the training dataset, whereas 80 CT images were used as the testing data. Coding was implemented in MATLAB. The optimization of the learning rate enhanced image segmentation accuracy by 2.23%. Iterative metaheuristic algorithms would lead to longer network training times. However, the proposed network optimization method could be very useful when large databases are not available for network training and higher accuracy is preferred over time savings.

References:

[1] Ronneberger, O., Fischer, P., & Brox, T. (2015). “U-NET: Convolutional Networks for Biomedical Image Segmentation”. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1505.04597.

[2] Shamim, S., Awan, M. J., Zain, A. M., Naseem, U., Mohammed, M. A., & Garcia-Zapirain, B. (2022). “Automatic COVID-19 Lung Infection Segmentation through Modified Unet Model”. Journal of Healthcare Engineering, 2022, 1–13. https://doi.org/10.1155/2022/6566982.

[3] Saood, A., & Hatem, I. (2021). “COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet”. BMC Medical Imaging, 21(1). https://doi.org/10.1186/s12880-020-00529-5.

[4] Kalane, P., Patil, S. D., Patil, B. A., & Sharma, D. P. (2021). “Automatic detection of COVID-19 disease using U-Net architecture based fully convolutional network”. Biomedical Signal Processing and Control, 67, 102518. https://doi.org/10.1016/j.bspc.2021.102518.

[5] Raj, A. N. J., Zhu, H., Khan, A., Zhuang, Z., Yang, Z., Mahesh, V. G. V., & Karthik, G. (2021). “ADID-UNET—a segmentation model for COVID-19 infection from lung CT scans”. PeerJ, 7, e349. https://doi.org/10.7717/peerj-cs.349.

[6] Kaur, S., Kumar, Y., Koul, A., & Kamboj, S. K. (2022). “A Systematic Review on Metaheuristic Optimization Techniques for feature selections in Disease Diagnosis”: Open Issues and Challenges. Archives of Computational Methods in Engineering, 30(3), 1863–1895. https://doi.org/10.1007/s11831-022-09853-1.

[7] Antoniou, A., & Lu, W. (2021). Practical Optimization: Algorithms and engineering applications. https://link.springer.com/content/pdf/10.1007/978-1-0716-0843-2.pdf.

[8] Wong, W., & Ming, C. I. (2019). “A Review on Metaheuristic Algorithms: Recent Trends, Benchmarking and Applications”. 2019 7th International Conference on Smart Computing & Communications (ICSCC). https://doi.org/10.1109/icscc.2019.8843624.

[9] Yang, D., Martinez, C., Visuña, L., Khandhar, H. M., Bhatt, C., & Carretero, J. (2021). “Detection and analysis of COVID-19 in medical images using deep learning techniques”. Scientific Reports, 11(1). https://doi.org/10.1038/s41598-021-99015-3.

[10] Elaziz, M. A., Dahou, A., Abualigah, L., Yu, L., Alshinwan, M., Khasawneh, A. M., & Lu, S. (2021). “Advanced metaheuristic optimization techniques in applications of deep neural networks: a review”. Neural Computing and Applications, 33(21),14079–14099. https://doi.org/10.1007/s00521-021-05960-5.

[11] Takase, T., Oyama, S., & Kurihara, M. (2018). “Effective neural network training with adaptive learning rate based on training loss”. Neural Networks, 101, 68–78. https://doi.org/10.1016/j.neunet.2018.01.016.

[12] Nematzadeh, S., Kiani, F., Torkamanian-Afshar, M., & Aydin, N. (2022). “Tuning hyperparameters of machine learning algorithms and deep neural networks using metaheuristics: A bioinformatics study on biomedical and biological cases”. Computational Biology and Chemistry, 97, 107619.https://doi.org/10.1016/j.compbiolchem.2021.107619.

[13] Kaveh, A., & Hamedani, K. B. (2022). “Advanced metaheuristic algorithms and their applications in structural optimization”. In Springer eBooks. https://doi.org/10.1007/978-3-031-13429-6.

[14] Li, C., Jiang, J., Zhao, Y., Li, R., Wang, E., Zhang, X., & Zhao, K. (2021). “Genetic Algorithm based hyper-parameters optimization for transfer Convolutional Neural Network”. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2103.03875.

[15] Thavasimani, K., & Srinath, N. K. (2022). “Hyperparameter optimization using custom genetic algorithm for classification of benign and malicious traffic on internet of things–23 dataset”. International Journal of Power Electronics and Drive Systems, 12(4), 4031. https://doi.org/10.11591/ijece.v12i4.pp4031-4041.

[16] Gaspar, A., Oliva, D., Cuevas, E., Zaldivar, D., Pérez, M. A., & Pajares, G. (2021). “Hyperparameter optimization in a convolutional neural network using metaheuristic algorithms”. In Studies in computational intelligence (pp. 37–59). https://doi.org/10.1007/978-3-030-70542-8_2.

[17] Okwu, M. O., & Tartibu, L. K. (2021). “Metaheuristic Optimization: Nature-Inspired algorithms swarm and computational intelligence, theory and applications”. In Studies in computational intelligence. https://doi.org/10.1007/978-3-030-61111-8.

[18] Saremi, S., Mirjalili, S., & Lewis, A. (2017). “Grasshopper Optimisation Algorithm: Theory and application”. Advances in Engineering Software, 105, 30–47. https://doi.org/10.1016/j.advengsoft.2017.01.004.

[19] El-Shorbagy, M. A., & El-Refaey, A. M. (2020). “Hybridization of Grasshopper optimization algorithm with genetic algorithm for solving system of Non-Linear equations”. IEEE Access, 8, 220944–220961. https://doi.org/10.1109/access.2020.3043029.

[20] Wang, R., Lei, T., Cui, R., Zhang, B., Meng, H., & Nandi, A. K. (2022). “Medical image segmentation using deep learning: A survey”. Iet Image Processing, 16(5), 1243–1267. https://doi.org/10.1049/ipr2.12419.

[21] Polat, H. (2022). “Multi-task semantic segmentation of CT images for COVID-19 infections using DeepLabV3+ based on dilated residual network”. Physical and Engineering Sciences in Medicine, 45(2), 443–455. https://doi.org/10.1007/s13246-022-01110-w.

[22] Kumar, G. M., & Parthasarathy, E. (2023). “Development of an enhanced U-Net model for brain tumor segmentation with optimized architecture”. Biomedical Signal Processing and Control,81,104427. https://doi.org/10.1016/j.bspc.2022.104427.

[23] Popat, V., Mahdinejad, M., Dalmau, O., Naredo, E., & Ryan, C. (2020). “GA-based U-Net Architecture Optimization Applied to Retina Blood Vessel Segmentation”. Proceedings of the 12th International Joint Conference on Computational Intelligence. SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/001011220192.