Improved COVID-19 detection with chest x-ray images using deep learning

Gupta, Vedika, Jain, Nikita, Sachdeva, Jatin, Gupta, Mudit, Mohan, Senthilkumar, Bajuri, Mohd Yazid and Ahmadian, Ali (2022) Improved COVID-19 detection with chest x-ray images using deep learning. Multimedia Tools and Applications. ISSN 1573-7721 (In Press)

[thumbnail of Improved COVID-19 detection with chest x-ray images.pdf] Text
Improved COVID-19 detection with chest x-ray images.pdf - Published Version
Restricted to Repository staff only

Download (2MB) | Request a copy


The novel coronavirus disease, which originated in Wuhan, developed into a severe public health problem worldwide. Immense stress in the society and health department was advanced due to the multiplying numbers of COVID carriers and deaths. This stress can be lowered by performing a high-speed diagnosis for the disease, which can be a crucial stride for opposing the deadly virus. A good large amount of time is consumed in the diagnosis. Some applications that use medical images like X-Rays or CT-Scans can pace up the time used in diagnosis. Hence, this paper aims to create a computer-aideddesign system that will use the chest X-Ray as input and further classify it into one of the three classes, namely COVID-19, viral Pneumonia, and healthy. Since the COVID-19 positive chest X-Rays dataset was low, we have exploited four pre-trained deep neural networks (DNNs) to find the best for this system. The dataset consisted of 2905 images with 219 COVID-19 cases, 1341 healthy cases, and 1345 viral pneumonia cases. Out of these images, the models were evaluated on 30 images of each class for the testing, while the rest of them were used for training. It is observed that AlexNet attained an accuracy of 97.6% with an average precision, recall, and F1 score of 0.98, 0.97, and 0.98, respectively

Item Type: Article
Keywords: COVID-19 | Chest X-ray | Deep learning | Transfer learning | Convolutional neural network (CNN) | Multi-class classification
Subjects: Social Sciences and humanities > Social Sciences > Social Sciences (General)
JGU School/Centre: Jindal Global Business School
Depositing User: Amees Mohammad
Date Deposited: 12 Aug 2022 05:56
Last Modified: 12 Aug 2022 05:56
Official URL:


Downloads per month over past year

Actions (login required)

View Item
View Item