Deep learning based computed tomography image classification of COVID-19 patients

Seethalakshmy, A.; Tamilvizhi, T.; Sowjanya, K. Naga; Bala, Bhoomeshwar

doi:10.47974/JIM-1668

Open Access Research Article

Deep learning based computed tomography image classification of COVID-19 patients

^*A. SeethalakshmyCorresponding authorapseethalakshmy@gmail.comDepartment of MathematicsSaveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai, IndiaView full profile → , T. Tamilvizhitamilvizhi.phd.it@gmail.comDepartment of Computer Science and EngineeringPanimalar Engineering CollegeChennai, IndiaView full profile → , K. Naga Sowjanyasowjanyakasi1@gmail.comDepartment of Information TechnologyVel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering CollegeChennai, IndiaView full profile → , Bhoomeshwar Baladrbhoom08@dtu.edu.etDepartment of Computer ScienceDebre Tabor UniversityDebra Tabor, EthiopiaView full profile →

* Corresponding author · click or hover a name for details

pp. 371–381Vol. 26Issue 3April 2023DOI: 10.47974/JIM-1668XML

Published Online:: 01 Apr 2023
Article type:: Research Article
Language:: EN
Article no.:: JIM-1668
Pages:: 371–381

Abstract

Accurate identification of the newest 2019 coronavirus (COVID-19) disease is required for effective illness behavior and management. To categorize and analyses COVID-19 in the prevalent region, for (COVID-19) detection using medical images, computed tomography (CT) imaging is informative, reliable, and quick. Chest CT images are readily available in nearly all hospitals, making it possible to use them to classify COVID-19 patients early on. When COVID-19 infection spreads quickly, a significant amount of time are required for the chest CT-based COVID-19 categorization. Since medical practitioners have scarce time, a computerized analysis of CT scans is required. In this paper, we construct a classification framework involving the extraction of CT image characteristics and categorization. The framework is separated into training and testing modules, with training the classifier aiding in the development of a model that efficiently classifies CT images as input. Deep convolutional neural network including VGG16, ResNet50, DensNet121, and inspectionResNetV2, variations were utilized as classifiers in the present investigation (DCNN) different machine learning and Statistical modeling techniques to identify COVID-19 infections probability and discover lesion for training.

Keywords

Convolutional neural network CT image classification Deep learning Transfer learning

Subject Classifications

Primary 93A30Secondary 49K15

References

[1] Harmon, S. A., et al. Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets. Nature communications, 11.1, pp. 1-7, (2020).
[2] Wang, Q., Yang, D., Li, Z., Zhang, X., & Liu, C. Deep regression via multi-channel multi-modal learning for pneumonia screening. IEEE Access, 8, pp. 78530-78541, (2020).
[3] Kingsley A. Ogudo, R. Surendran, Osamah Ibrahim Khalaf, “Optimal Artificial Intelligence Based Automated Skin Lesion Detection and Classification Model”, Computer Systems Science and Engineering, 44.1, pp. 693-707, (2023).
[4] Zhou, L., Li, Z., Zhou, J., Li, H., Chen, Y., Huang, Y. & Gao, X. A rapid, accurate and machine-agnostic segmentation and quantification method for CT-based COVID-19 diagnosis. IEEE transactions on medical imaging, 39.8, pp. 2638-2652, (2020).
[5] Wang, X., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H. & Zheng, C. A weakly-supervised framework for COVID-19 classification and lesion localization from chest CT. IEEE transactions on medical imaging, 39.8, pp. 2615-2625, (2020).
[6] Surendran R. and Parvatha Varthini B., Inject an Elastic Grid Computing Techniques to Optimal Resource Management Technique Operations, Journal of Computer Science, vol. 9, no. 8, pp. 1051-1060, (2013).
[7] Zhang, K., Liu, X., Shen, J., Li, Z., Sang, Y., Wu, X., & Wang, G. Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography. Cell, 181.6, pp. 1423-1433, (2020).
[8] Swapnarekha, H., et al. Covid CT-net: A deep learning framework for COVID-19 prognosis using CT images. Journal of Interdisciplinary Mathematics, vol. 24, no. 2, pp. 327-352, (2021).
[9] Tamilvizhi, T., Surendran, R., Anbazhagan, K., Rajkumar, K. Quantum Behaved Particle Swarm Optimization-Based Deep Transfer Learning Model for Sugarcane Leaf Disease Detection and Classification, Mathematical Problems in Engineering, 2022, pp.1-12, (2022).
[10] Jain, R., Gupta, M., Jain, K., Kang, S. Deep learning based prediction of COVID-19 virus using chest X-Ray, Journal of Interdisciplinary Mathematics, vol. 24, no.1, (2021).
[11] Shahin, O. R., Abd El-Aziz, R. M., & Taloba, A. I. Detection and classification of Covid-19 in CT-lungs screening using machine learning techniques. Journal of Interdisciplinary Mathematics, vol. 25, no. 3, 791-813 (2022).
[12] Jee G, Harshvardhan GM, Gourisaria MK. Juxtaposing inference capabilities of deep neural models over posteroanterior chest radiographs facilitating COVID-19 detection. Journal of Interdisciplinary Mathematics. vol. 24, no. 2, pp 299-325, (2021).

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