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

Authors

  • Mallisetty Siva Mahesh Student, Department of Information Technology, Vasireddy Venkatadri Institute of Technology, Guntur, Nambur, India Author
  • Kattamuri B N Ayyappa Student, Department of Information Technology, Vasireddy Venkatadri Institute of Technology, Guntur, Nambur, India Author
  • Maddela Murali Student, Department of Information Technology, Vasireddy Venkatadri Institute of Technology, Guntur, Nambur, India Author
  • Mididoddi Surendra Babu Student, Department of Information Technology, Vasireddy Venkatadri Institute of Technology, Guntur, Nambur, India Author
  • Nagababu Pachhal Assistant Professor, Department of Information Technology, Vasireddy Venkatadri Institute of Technology, Guntur, Nambur, India Author

Keywords:

CNNResnet50, VGG16, Grad-CAM, Covid

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. 

Downloads

Download data is not yet available.

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

Published

2023-11-30

Issue

Vol. 11 No. 6 (2023): International Journal of Innovative Research in Computer Science and Technology (Nov) 2023

Section

Articles

How to Cite

Evaluating Median Accuracy of ResNet50 and VGG16 Models in COVID-19 Detection . (2023). International Journal of Innovative Research in Computer Science & Technology, 11(6), 6–12. Retrieved from https://www.acspublisher.com/journals/index.php/ijircst/article/view/11582