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A Novel Hybrid Optimization Enabled Densenet for Covid-19 Classification using CT Images

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Abstract

Corona Virus disease 2019 (Covid-19) is an acute disease that affects the respiratory system alveolar of the human, which leads to serious illness and may cause death. When an infected person coughs, sneezes, speaks, sings, or breathes, the virus can spread from their mouth or nose in minute liquid particles. Disease diagnosis using Computed Tomography (CT) images is widely utilized to detect infection; still, accurate detection is a challenging task. Hence, this research introduces a novel hybrid optimization enabled DenseNet for Covid-19 classification. The hybrid optimization, named Gradient Mutated Leader Algorithm (GMLA) is proposed for tuning the weights of DenseNet by combining the Mutated leader's behavior in guiding the group member in position updation with the gradient descent algorithm for the acquisition of computation efficiency with stable convergence, which helps to obtain the global best solution for tuning the DenseNet's weights to make the classification more accurate. In addition, the proposed GMLA_DenseNet utilizes the data augmentation technique using rotating, shifting, zooming, and flipping to obtain balanced data with enormous data sizes, which makes the classification more efficient. The performance of GMLA_DenseNet is evaluated using True Negative Rate (TNR), True Positive Rate (TPR), Precision, and segmentation accuracy and obtained the maximal values of 0.920, 0.919, 0.919, and 0.919, respectively.

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Data Availability

The data underlying this article is UCSD-AI4H/COVID-CT dataset taken from, "https://github.com/UCSD-AI4H/COVID-CT", accessed on June 2022.

References

  1. Jiang, Y., Chen, H., Loew, M., & Ko, H. (2020). COVID-19 CT image synthesis with a conditional generative adversarial network. IEEE Journal of Biomedical and Health Informatics, 25(2), 441–452.

    Article  Google Scholar 

  2. Guan, W. J., Ni, Z. Y., Hu, Y., Liang, W. H., Ou, C. Q., He, J. X., Liu, L., Shan, H., Lei, C. L., Hui, D. S., & Du, B. (2020). Clinical characteristics of coronavirus disease 2019 in China. New England Journal of Medicine, 382(18), 1708–1720.

    Article  Google Scholar 

  3. Hu, S., Gao, Y., Niu, Z., Jiang, Y., Li, L., Xiao, X., Wang, M., Fang, E. F., Menpes-Smith, W., Xia, J., & Ye, H. (2020). Weakly supervised deep learning for covid-19 infection detection and classification from CT images. IEEE Access, 8, 118869–118883.

    Article  Google Scholar 

  4. Turkoglu, M. (2021). COVID-19 detection system using chest CT images and multiple kernels-extreme learning machine based on deep neural network. IRBM, 42(4), 207–214.

    Article  Google Scholar 

  5. Perumal, V., Narayanan, V., & Rajasekar, S. J. S. (2021). Detection of COVID-19 using CXR and CT images using transfer learning and haralick features. Applied Intelligence, 51(1), 341–358.

    Article  Google Scholar 

  6. Shamrat, F. J. M., Azam, S., Karim, A., Ahmed, K., Bui, F. M., & De Boer, F. (2023). High-precision multiclass classification of lung disease through customized MobileNetV2 from chest X-ray images. Computers in Biology and Medicine, 155, 106646.

    Article  Google Scholar 

  7. Shamrat, F. J. M., Azam, S., Karim, A., Islam, R., Tasnim, Z., Ghosh, P., & De Boer, F. (2022). LungNet22: A fine-tuned model for multiclass classification and prediction of lung disease using X-ray images. Journal of Personalized Medicine, 12(5), 680.

    Article  Google Scholar 

  8. Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., Tao, Q., Sun, Z., & Xia, L. (2020). Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology, 296(2), 32–40.

    Article  Google Scholar 

  9. Fang, Y., Zhang, H., Xie, J., Lin, M., Ying, L., Pang, P., & Ji, W. (2020). Sensitivity of chest CT for COVID-19: Comparison to RT-PCR. Radiology, 296, 115–117.

    Article  Google Scholar 

  10. Aminian, A., Safari, S., Razeghian-Jahromi, A., Ghorbani, M., & Delaney, C. P. (2020). COVID-19 outbreak and surgical practice: Unexpected fatality in perioperative period. Annals of Surgery. https://doi.org/10.1097/SLA.0000000000003925

    Article  Google Scholar 

  11. Chan, J. F. W., Yuan, S., Kok, K. H., To, K. K. W., Chu, H., Yang, J., Xing, F., Liu, J., Yip, C. C. Y., Poon, R. W. S., & Tsoi, H. W. (2020). A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: A study of a family cluster. The Lancet, 395(10223), 514–523.

    Article  Google Scholar 

  12. Gao, K., Su, J., Jiang, Z., Zeng, L. L., Feng, Z., Shen, H., Rong, P., Xu, X., Qin, J., Yang, Y., & Wang, W. (2021). Dual-branch combination network (DCN): Towards accurate diagnosis and lesion segmentation of COVID-19 using CT images. Medical Image Analysis, 67, 101836.

    Article  Google Scholar 

  13. Liu, Q., Leung, C. K., & Hu, P. (2020). A two-dimensional sparse matrix profile DenseNet for COVID-19 diagnosis using chest CT images. IEEE Access, 8, 213718–213728.

    Article  Google Scholar 

  14. Udugama, B., Kadhiresan, P., Kozlowski, H. N., Malekjahani, A., Osborne, M., Li, V. Y., Chen, H., Mubareka, S., Gubbay, J. B., & Chan, W. C. (2020). Diagnosing COVID-19: The disease and tools for detection. ACS Nano, 14(4), 3822–3835.

    Article  Google Scholar 

  15. Ying, X., Guo, H., Ma, K., Wu, J., Weng, Z. and Zheng, Y., X2CT-GAN: reconstructing CT from biplanar X-rays with generative adversarial networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10619–10628 (2019).

  16. Akter, S., Shamrat, F. M. J. M., Chakraborty, S., Karim, A., & Azam, S. (2021). COVID-19 detection using deep learning algorithm on chest X-ray images. Biology, 2021(10), 1174.

    Article  Google Scholar 

  17. Arivoli, A., Golwala, D., & Reddy, R. (2022). CoviExpert: COVID-19 detection from chest X-ray using CNN. Measurement Sensors, 22, 100392.

    Article  Google Scholar 

  18. Zhang, Z., Romero, A., Muckley, M.J., Vincent, P., Yang, L. and Drozdzal, M., Reducing uncertainty in undersampled MRI reconstruction with active acquisition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2049–2058 (2019).

  19. Yan, K., Peng, Y., Sandfort, V., Bagheri, M., Lu, Z. and Summers, R.M., Holistic and comprehensive annotation of clinically significant findings on diverse CT images: learning from radiology reports and label ontology. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8523–8532, (2019).

  20. Ragab, M., Alshehri, S., Alhakamy, N. A., Mansour, R. F., & Koundal, D. (2022). Multiclass classification of chest X-ray images for the prediction of COVID-19 using capsule network. Computational Intelligence and Neuroscience, 2022, 4565214. https://doi.org/10.1155/2022/6185013

    Article  Google Scholar 

  21. Zhang, M., Wu, Q., Chen, H., Heidari, A. A., Cai, Z., Li, J., & Mansour, R. F. (2023). Whale optimization with random contraction and Rosenbrock method for COVID-19 disease prediction. Biomedical Signal Processing and Control, 83, 104638.

    Article  Google Scholar 

  22. Rajaraman, S., Candemir, S., Kim, I., Thoma, G., & Antani, S. (2018). Visualization and interpretation of convolutional neural network predictions in detecting pneumonia in pediatric chest radiographs. Applied Sciences, 8(10), 1715.

    Article  Google Scholar 

  23. Xue, Yu., Onzo, B.-M., Mansour, R. F., & Shoubao, Su. (2022). Deep convolutional neural network approach for COVID-19 detection. Computer Systems Science and Engineering, 42(1), 201–211.

    Article  Google Scholar 

  24. Song, Y., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z., Chen, J., Wang, R., Zhao, H., Chong, Y., & Shen, J. (2021). Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. IEEE/ACM transactions on computational biology and bioinformatics, 18(6), 2775–2780.

    Article  Google Scholar 

  25. Shamsi, A., Asgharnezhad, H., Jokandan, S. S., Khosravi, A., Kebria, P. M., Nahavandi, D., Nahavandi, S., & Srinivasan, D. (2021). An uncertainty-aware transfer learning-based framework for covid-19 diagnosis. IEEE Transactions on Neural Networks and Learning Systems, 32(4), 1408–1417.

    Article  Google Scholar 

  26. Singh, A. K., Kumar, A., Mahmud, M., Kaiser, M. S., & Kishore, A. (2021). COVID-19 infection detection from chest X-ray images using hybrid social group optimization and support vector classifier. Cognitive Computation, 56, 1–13.

    Google Scholar 

  27. UCSD-AI4H/COVID-CT dataset taken from, "https://github.com/UCSD-AI4H/COVID-CT", accessed on June (2022).

  28. Kavitha, P. and Prabakaran, S., A Novel Hybrid Segmentation Method with Particle Swarm Optimization and Fuzzy C-mean Based on Partitioning the Image for Detecting Lung Cancer (2019).

  29. Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N. and Liang, J., Unet++: A nested u-net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 3–11, (2018).

  30. Baby, Y. R., & Sumathy, V. K. R. (2020). Kernel-based Bayesian clustering of computed tomography images for lung nodule segmentation. IET Image Process., 14(5), 890–900.

    Article  Google Scholar 

  31. Data augmentation, “https://www.geeksforgeeks.org/python-data-augmentation/”, last accessed on September (2022).

  32. Zulpe, N., & Pawar, V. (2012). GLCM textural features for brain tumor classification. International Journal of Computer Science Issues (IJCSI), 9(3), 354.

    Google Scholar 

  33. Zeng, H., Zhang, R., Huang, M., & Wang, X. (2015). Compact local directional texture pattern for local image description. Advances in Multimedia, 2015, 8–8.

    Article  Google Scholar 

  34. Huang, G., Liu, Z., Van Der Maaten, L. and Weinberger, K.Q., Densely connected convolutional networks In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708, (2017).

  35. Chauhan, T., Palivela, H., & Tiwari, S. (2021). Optimization and fine-tuning of DenseNet model for classification of Covid-19 cases in medical imaging. International Journal of Information Management Data Insights, 1(2), 100020.

    Article  Google Scholar 

  36. Zeidabadi, F. A., Doumari, S. A., Dehghani, M., Montazeri, Z., Trojovsky, P., & Dhiman, G. (2022). MLA: A new mutated leader algorithm for solving optimization problems. Computers, Materials & Continua, 70(3), 5631–5649.

    Article  Google Scholar 

  37. Ruder, S., "An overview of gradient descent optimization algorithms", arXiv preprint arXiv:1609.04747 (2016).

  38. Yang, H., Zhang, J., Zhao, Y., Ji, Y., Han, J., Lin, Y., & Lee, Y. (2015). CSO: Cross stratum optimization for optical as a service. IEEE Communications Magazine, 53(8), 130–139.

    Article  Google Scholar 

  39. Askari, Q., Younas, I., & Saeed, M. (2020). Political Optimizer: A novel socio-inspired meta-heuristic for global optimization. knowledge-based systems, 195, 105709.

    Article  Google Scholar 

  40. Das, B., Mukherjee, V., & Das, D. (2020). Student psychology based optimization algorithm: A new population based optimization algorithm for solving optimization problems. Advances in Engineering software, 146, 102804.

    Article  Google Scholar 

  41. Saha, S., Mou, L., Qiu, C., Zhu, X. X., Bovolo, F., & Bruzzone, L. (2020). Unsupervised deep joint segmentation of multitemporal high-resolution images. IEEE Transactions on Geoscience and Remote Sensing, 58(12), 8780–8792.

    Article  Google Scholar 

  42. Saood, A., & Hatem, I. (2021). COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet. BMC Medical Imaging, 21(1), 1–10.

    Article  Google Scholar 

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Acknowledgements

I would like to express my very great appreciation to the co-authors of this manuscript for their valuable and constructive suggestions during the planning and development of this research work.

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This research did not receive any specific funding.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Annamalai University, Annamalai Nagar, Chidambaram, Tamil Nadu, 608002, India

    S. Karthi

  2. Department of Computer Science and Engineering, Annamalai University, Annamalai Nagar, Chidambaram, Tamil Nadu, 608002, India

    L. R. Sudha

  3. Department of Computer Science and Engineering, St Joseph College of Engineering, Tamil Nadu, Chennai, India

    M. Navaneetha Krishnan

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All authors have made substantial contributions to conception and design, revising the manuscript, and the final approval of the version to be published. Also, all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Karthi, S., Sudha, L.R. & Navaneetha Krishnan, M. A Novel Hybrid Optimization Enabled Densenet for Covid-19 Classification using CT Images. Sens Imaging 24, 31 (2023). https://doi.org/10.1007/s11220-023-00434-5

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