COVID-19 Image Classification Using VGG-16 & CNN based on CT Scans

Authors

  • Shivasangaiah V Hiremath  PG Scholar, Computer Science & Engineering, Er. Perumal Manimekalai College of Engineering, Hosur, India
  • Mr. P. Yogananth  Assistant Professor, Computer Science & Engineering, Er. Perumal Manimekalai College of Engineering, Hosur, India

Keywords:

Visual Geometry Group-VGG16, Convolutional Neural Network-CNN, CT Images, X-Ray, rTPCR

Abstract

The main purpose of this work is to investigate and compare several deep learning enhanced techniques applied to CT-scan medical images for the detection of COVID-19. In this proposed work we are going to build two Covid19 Image classification models. Both the model uses Lungs CT Scan images to classify the covid-19. We build the first Classification model using VGG16 Transfer leaning framework and second model using Deep Learning Technique Convolutional Neural Network CNN to classify and diagnose the disease and we able to achieve the best accuracy in both the model.

References

  1. N. Cheung, G. Tikellis, and J. J. Wang, “Diabetic retinopathy.” Ophthalmology, vol. 114, no. 11, p. 2098, 2010.
  2. S. R. Flaxman, R. R. Bourne, S. Resnikoff, P. Ackland, T. Braithwaite, M. V. Cicinelli, A. Das, J. B. Jonas, J. Keeffe, J. H. Kempen et al.,“Global causes of blindness and distance vision impairment 1990–2020:a systematic review and meta-analysis,” The Lancet Global Health, vol. 5,no. 12, pp. e1221–e1234, 2017.
  3. A. Ahmad, A. B. Mansoor, R. Mumtaz, M. Khan, and S. H. Mirza,“Image processing and classification in diabetic retinopathy: A review,”in European Workshop on Visual Information Processing, 2015, pp. 1–6.
  4. E. M. Shahin, T. E. Taha, W. Al-Nuaimy, S. E. Rabaie, O. F. Zahran, andF. E. A. El-Samie, “Automated detection of diabetic retinopathy in blurred digital fundus images,” in Computer Engineering Conference, 2013, pp.20–25.
  5. H. F. Jaafar, A. K. Nandi, and W. Al-Nuaimy, “Automated detection andgrading of hard exudates from retinal fundus images,” in Signal ProcessingConference, 2011 European, 2011, pp. 66–70.
  6. R. Casanova, S. Saldana, E. Y. Chew, R. P. Danis, C. M. Greven, and W. T. Ambrosius, “Application of random forests methods to diabetic retinopathy classification analyses,” Plos One, vol. 9, no. 6, p. e98587,2014.
  7. V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, and J. Cuadros, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.” Jama, vol. 316, no. 22, p. 2402, 2016.
  8. R. Gargeya and T. Leng, “Automated identification of diabetic retinopathy using deep learning,” Ophthalmology, vol. 124, no. 7, pp. 962–969, 2017.
  9. Kaggle, “Diabetic retinopathy detection,” https://www.kaggle.com/c/diabetic-retinopathy-detection/, July 27, 2015, accessed May 7, 2018.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2022-08-30

Issue

Volume 8, Issue 4, July-August-2022

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Shivasangaiah V Hiremath, Mr. P. Yogananth, " COVID-19 Image Classification Using VGG-16 & CNN based on CT Scans, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 4, pp.218-224, July-August-2022.