Download pdf
Pretrained-ResNet-based COVID-19 CT image classification
- 1 Minzu University of China, No. 27, Zhongguancun South Street, Haidian District, Beijing, China
* Author to whom correspondence should be addressed.
Abstract
Abstract. The COVID-19 pandemic has significantly affected global public health and the economy. Clinically, compared to nucleic acid testing, CT imaging offers a more intuitive display of disease progression, particularly in cases where patients exhibit atypical symptoms or when nucleic acid test results are inconclusive. In such scenarios, CT imaging serves as a valuable supplementary diagnostic tool. However, the task of classifying COVID-19 CT images presents numerous challenges. Firstly, the imaging features associated with COVID-19 exhibit significant heterogeneity, often overlapping with those of other pulmonary diseases such as pneumonia or tuberculosis, which complicates the classification process. Additionally, CT images typically contain noise and artifacts that can interfere with The model's capability to accurately differentiate between various conditions. This paper proposes a ResNet-based method for the classification of COVID-19 CT images. Experimental results demonstrate that the ResNet-based approach offers significant advantages, delivering high accuracy and strong generalization capabilities in classifying images related to COVID-19.
Keywords
Keywords: COVID-19, CNN, ResNet, Computer vision, Medical image, Image classification.
[1]. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998.
[2]. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," in Advances in Neural Information Processing Systems, vol. 25, pp. 1097-1105, 2012.
[3]. Schober, P. and T. R. Vetter, "Logistic regression in medical research," Anesthesia & Analgesia, vol. 132, no. 2, pp. 365-366, 2021.
[4]. D. Mustafa Abdullah and A. . Mohsin Abdulazeez, “Machine Learning Applications based on SVM Classification A Review”, QAJ, vol. 1, no. 2, pp. 81–90, Apr. 2021.
[5]. A. N. Elmachtoub, J. C. N. Liang, and R. McNellis, "Decision trees for decision-making under the predict-then-optimize framework," in Proc. Int. Conf. Mach. Learn., PMLR, Nov. 2020, pp. 2858-2867.
[6]. F. I. Adiba, T. Islam, M. S. Kaiser, et al., "Effect of corpora on classification of fake news using naive Bayes classifier," Int. J. Autom. Artif. Intell. Mach. Learn., vol. 1, no. 1, pp. 80-92, 2020.
[7]. A. Esteva et al., "A guide to deep learning in healthcare," Nature Medicine, vol. 29, pp. 27-38, 2023.
[8]. S. Liu and X. Wang, "An improved method of medical images classification based on contrast learning," J. Jilin Univ. (Inf. Sci. Ed.), 2024.
[9]. M.E.H. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M.A. Kadir, Z.B. Mahbub, K.R. Islam, M.S. Khan, A. Iqbal, N. Al-Emadi, M.B.I. Reaz, M. T. Islam, “Can AI help in screening Viral and COVID-19 pneumonia?” IEEE Access, Vol. 8, 2020, pp. 132665 - 132676.
[10]. Rahman, T., Khandakar, A., Qiblawey, Y., Tahir, A., Kiranyaz, S., Kashem, S.B.A., Islam, M.T., Maadeed, S.A., Zughaier, S.M., Khan, M.S. and Chowdhury, M.E., 2020. Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-ray Images.
Cite this article
Sun,S. (2024).Pretrained-ResNet-based COVID-19 CT image classification.Theoretical and Natural Science,62,190-196.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content
About volume
Volume title: Proceedings of the 4th International Conference on Biological Engineering and Medical Science
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).