Abstract: Effective screening and early detection of COVID-19 patients are highly crucial to slow down and stop the disease's rapid spread at this time. Currently, RT-PCR, CT scanning and Chest X-ray (CXR) imaging are the diagnosis mechanisms for COVID-19 detection. In this proposed work radiology examination by using CXR images is used for COVID-19 detection due to dearth of CT Scanners and RT-PCR testing centers. Therefore, researchers have developed various Deep and Machine Learning systems that can predict COVID-19 using CXR images. Out of which, few are exhibited good prediction results. However, Most of the models are suffered with over fitting, high variance, memory and generalization errors which are caused by noise as well as datasets are limited. Therefore, a Convolutional Neural Network (CNN) with the leveraging Efficient Net architecture is proposed for COVID-19 case detection. The proposed methods have an accuracy of 99% which gives the better results than the present available methods. Therefore, the proposed model can be used in real-time covid-19 classification systems.

Chalapathiraju Kanumuri, CH. Renu Madhavi and Torthi Ravichandra, “Novel Approach in Classification and Prediction of COVID-19 from Radiograph Images using CNN” International Journal of Advanced Computer Science and Applications(IJACSA), 13(9), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130966

@article{Kanumuri2022,
title = {Novel Approach in Classification and Prediction of COVID-19 from Radiograph Images using CNN},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130966},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130966},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {9},
author = {Chalapathiraju Kanumuri and CH. Renu Madhavi and Torthi Ravichandra}
}