Evaluating Median Accuracy of ResNet50 and VGG16 Models in COVID-19 Detection

Mallisetty Siva Mahesh; Kattamuri B N Ayyappa; Maddela Murali; Mididoddi Surendra Babu; Nagababu Pachhala

doi:10.55524/ijircst.2023.11.6.2

Abstract

The increasing number of Covid-19 cases and the lack of reliable, quick-to-use testing tools herald a new era in X-ray analysis employing deep learning methods. The Covid-19 virus's emergence poses a threat to human existence. Therefore, it will take time to develop a quick and accurate method of identifying the Covid-19 virus in patients. The reference method is the conventional RT-PCR technique. The goal of this research is to create an automated system that uses CNN models such as Resnet50, VGG16, and Grad-CAM to analyze X-ray images in order to provide a reliable and effective method of diagnosing Covid-19 infection. The created models use image processing techniques to pre-process the X-ray picture. Afterwards, deep learning is used to classify the images after they have been segmented and transformed. The CNN model that is being used provides strong classification accuracy and shows the location in the lung where the disease is attacked, even for a normal person, we can anticipate the likelihood of where the COVID may affect them. Our model utilizes a convolution neural network that is trained on the standard COVID-19 Radiography Dataset.

Keywords

CNNResnet50,VGG16, Grad-CAM, Covid.

References

Ai T et al (2020) Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology: 200642
Ali A, Zhu Y, Zakarya M (2021) A data aggregation-based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing.Multimed Tools Appl. https://doi.org/10. 1007/s11042-020-10486-4
Apostolopoulos ID, Mpesiana TA (2020) Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med 43(2):635–640
Axell-House DB, Lavingia R, Rafferty M, Clark E, Amirian ES, Chiao EY (2020) The estimation of diagnostic accuracy of tests for COVID-19: A scoping review. J Infect 81(5):681–697
Bloice MD, Roth PM, Holzinger A (2019) Biomedical image augmentation using Augmentor. Bioinformatics 35(21):4522–4524.
Cohen J, Paul et al (2020) Covid-19 image data collection: Prospective predictions are the future. ArXiv preprint arXiv:2006.11988
covid-chest xray-dataset. https://github.com/ieee8023/COVID-chestxray-dataset. Accessed 25 Mar 2020
Eurosurveillance Editorial Team (2020) Note from the editors: World Health Organization declares novel corona virus (2019-nCoV) sixth public health emergency of international concern. Eurosurveillance 25(5): 200131e
Gazzah S, Bencharef O (2020) A Survey on how computer vision can response to urgent need to contribute in COVID-19 pandemics. 2020 International Conference on Intelligent Systems and Vision C (ISCV). IEEE, New York
Globalpulse. Need for greater cooperation between practitioners and the AI community. https://www. unglobalpulse.org/2020/05/need-for-greater-cooperation-between-practitioners-and-the-ai-community/. Accessed 27 May 2020
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
Holzinger A et al (2018) Current advances, trends and challenges of machine learning and knowledge extraction: from machine learning to explainable AI. International Cross-Domain Conference for Machine Learning and Knowledge Extraction. Springer, Cham, 2018
Holzinger A et al (2019) Causability and explainability of artificial intelligence in medicine. Wiley Inter discRev Data Min Knowl Discov 9(4):e1312
Isa A. Computational intelligence methods in medical image-based diagnosis of COVID-19 infections. Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis. Springer, Singapore, pp 251–270
Kallianos K et al (2019) How far have we come? Artificial intelligence for chest radiograph interpretation. Clin Radiol 74(5):338–345
Khan A, Sohail A, Zahoora U, Qureshi AS (2020) A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev 53(8):5455–5516
Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84–90
Lalkhen AG, McCluskey A (2008) Clinical tests: sensitivity and specificity. Contin Educ Anaesth Crit Care Pain 8(6):221–223

Cites this article as

[Mallisetty Siva Mahesh, Kattamuri B N Ayyappa, Maddela Murali, Mididoddi Surendra Babu, Nagababu Pachhala (2023), Evaluating Median Accuracy of ResNet50 and VGG16 Models in COVID-19 Detection, International Journal of Innovative Research in Computer Science and Technology (IJIRCST), Vol-11, Issue-6, Page No-6-12], (ISSN 2347 - 5552). www.ijircst.org

Corresponding Author

Mallisetty Siva Mahesh

Student, Department of Information Technology, Vasireddy Venkatadri Institute of Technology, Guntur, Nambur, India

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