Abstract
Introduction: The provision of medical facilities needed for COVID-19 diagnosis is a global concern. They must be a powerful tool for detecting and diagnosing the virus quickly using a variety of tests, as well as low-cost advancements. Whereas a chest X-ray image is an effective screening technique, the image acquisition by the instruments must be read appropriately and quickly if multiple tests are performed.
Objectives: COVID-19 causes continuous respiratory parenchymal ground glass and integrates respiratory opacity, with a curved shape and peripheral pulmonary dissemination in some cases, which is difficult to anticipate earlier on. In this chapter, we intend to construct a good platform to identify COVID-19 characteristics from the image of chest X-ray to aid in early analysis.
Methods: In particular, based on the Cuckoo search method, this chapter provides a bioinspired CNN-based model for COVID-19 diagnosis. This method identifies different deep learning strategies of COVID-19 patients’ chest X-ray images for accurate infection identification. The suggested model’s performance is estimated using the Cuckoo search approach. Furthermore, the bioinspired CNN characteristics are fine-tuned using optimization algorithm. Rigorous testing reveals that suggested method may accurately categorize chest X-ray images with high performance, remembrance, and sensitivity. Results: As a result, the suggested approach can be used to classify COVID-19 diseases from chest X-ray images in real time and also accuracy will be validated.
Conclusion: Finally, the investigation of comparison results illustrates the Cuckoo algorithm is realized to determine the interested regions of the COVID-19 x-ray images.
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References
Singh, A. K., Kumar, A., Mufti Mahmud, M., Kaiser, S., & Kishore, A. (2021). COVID-19 infection detection from chest X-ray images using hybrid social group optimization and support vector classifier. Cognitive Computation, 1–13.
Dhiman, G., Chang, V., Singh, K. K., & Shankar, A. (2021). Adopt: Automatic deep learning and optimization-based approach for detection of novel coronavirus covid-19 disease using x-ray images. Journal of Biomolecular Structure and Dynamics, 1–13.
Anter, A. M., Oliva, D., Thakare, A., & Zhang, Z. (2021). AFCM-LSMA: New intelligent model based on LĂ©vy slime mould algorithm and adaptive fuzzy C-means for identification of COVID-19 infection from chest X-ray images. Advanced Engineering Informatics, 49, 101317.
Altan, A., & Karasu, S. (2020). Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique. Chaos, Solitons & Fractals, 140, 110071.
Elaziz, M. A., Hosny, K. M., Salah, A., Darwish, M. M., Songfeng, L., & Sahlol, A. T. (2020). New machine learning method for image-based diagnosis of COVID-19. PLoS One, 15(6), e0235187.
Dev, K., Khowaja, S. A., Bist, A. S., Saini, V., & Bhatia, S. (2021). Triage of potential COVID-19 patients from chest X-ray images using hierarchical convolutional networks. Neural Computing and Applications, 1–16.
Dhiman, G., Kumar, V. V., Kaur, A., & Sharma, A. (2021). DON: Deep learning and optimization-based framework for detection of novel coronavirus disease using X-ray images. Interdisciplinary Sciences: Computational Life Sciences, 1–13.
Kavitha, S., & Inbarani, H. (2021). Bayes wavelet-CNN for classifying COVID-19 in chest X-ray images. In Computational vision and bio-inspired computing (pp. 707–717). Springer.
Pathan, S., Siddalingaswamy, P. C., & Ali, T. (2021). Automated detection of Covid-19 from chest X-ray scans using an optimized CNN architecture. Applied Soft Computing, 104, 107238.
El-Kenawy, El-Sayed, M., Mirjalili, S., Ibrahim, A., Alrahmawy, M., El-Said, M., Zaki, R. M., & Metwally Eid, M. (2021). Advanced meta-heuristics, convolutional neural networks, and feature selectors for efficient COVID-19 X-ray chest image classification. IEEE Access, 9, 36019–36037.
Alorf, A. (2021). The practicality of deep learning algorithms in COVID-19 detection: Application to chest X-ray images. Algorithms, 14(6), 183.
Vrbančič, G., Pečnik, Š., & Podgorelec, V. (2020). Identification of COVID-19 X-ray images using CNN with optimized tuning of transfer learning. In 2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) (pp. 1–8). IEEE.
Bahgat, W. M., Balaha, H. M., AbdulAzeem, Y., & Badawy, M. M. (2021). An optimized transfer learning-based approach for automatic diagnosis of COVID-19 from chest x-ray images. PeerJ Computer Science, 7, e555.
Rajpal, S., Lakhyani, N., Singh, A. K., Kohli, R., & Kumar, N. (2021). Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images. Chaos, Solitons & Fractals, 145, 110749.
Toğaçar, M., Ergen, B., & Cömert, Z. (2020). COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches. Computers in Biology and Medicine, 121, 103805.
Balachandar, A., Santhosh, E., Suriyakrishnan, A., Vigensh, N., Usharani, S., & Manju Bala, P. (2021). Deep learning technique based visually impaired people using YOLO V3 framework mechanism. In 2021 3rd International Conference on Signal Processing and Communication (ICPSC) (pp. 134–138). IEEE.
Gopalakrishnan, A., Manju Bala, P., & Ananth Kumar, T. (2020). An advanced bio-inspired shortest path routing algorithm for SDN controller over VANET. In 2020 International Conference on System, Computation, Automation and Networking (ICSCAN) (pp. 1–5). IEEE.
Thompson, R. N. (2020). Novel coronavirus outbreak in Wuhan, China, 2020: Intense surveillance is vital for preventing sustained transmission in new locations. Journal of Clinical Medicine, 9(2), 1–8.
Ucar, F., & Korkmaz, D. (2020). COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Medical Hypotheses, 140, 109761.
Shams, M. Y., Elzeki, O. M., Elfattah, M. A., Medhat, T., & Hassanien, A. E. (2020). Why are generative adversarial networks vital for deep neural networks? A case study on COVID-19 chest X-ray images. In Big data analytics and artificial intelligence against COVID-19: Innovation vision and approach (pp. 147–162). Springer.
Pereira, R. M., Bertolini, D., Teixeira, L. O., Silla Jr, C. N., & Costa, Y. M. G. (2020). COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios. Computer Methods and Programs in Biomedicine, 194, 105532.
Cohen, J. P., Morrison, P., & Dao, L. (2020). COVID-19 image data collection. arXiv:2003.11597.
Kermany, D., Zhang, K., & Goldbaum, M. (2018). Labeled optical coherence tomography (OCT) and chest X-ray images for classification. Mendeley data, 2.
Reshi, A. A., Rustam, F., Mehmood, A., Alhossan, A., Alrabiah, Z., Ahmad, A., Alsuwailem, H., & Choi, G. S. (2021). An efficient CNN model for COVID-19 disease detection based on X-ray image classification. Complexity, 2021, 1.
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Bala, P.M., Usharani, S., Rajmohan, R., Kumar, T.A., Balachandar, A. (2023). Bioinspired CNN Approach for Diagnosing COVID-19 Using Images of Chest X-Ray. In: Kumar, B.V., Sivakumar, P., Surendiran, B., Ding, J. (eds) Smart Computer Vision. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-20541-5_8
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