A comparative study of multiple neural network for detection of COVID-19 on chest X-ray

Shazia, Anis and Xuan, Tan Zi and Chuah, Joon Huang and Usman, Juliana and Qian, Pengjiang and Lai, Khin Wee (2021) A comparative study of multiple neural network for detection of COVID-19 on chest X-ray. Eurasip Journal on Advances in Signal Processing, 2021 (1). ISSN 1687-6180, DOI https://doi.org/10.1186/s13634-021-00755-1.

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Coronavirus disease of 2019 or COVID-19 is a rapidly spreading viral infection that has affected millions all over the world. With its rapid spread and increasing numbers, it is becoming overwhelming for the healthcare workers to rapidly diagnose the condition and contain it from spreading. Hence it has become a necessity to automate the diagnostic procedure. This will improve the work efficiency as well as keep the healthcare workers safe from getting exposed to the virus. Medical image analysis is one of the rising research areas that can tackle this issue with higher accuracy. This paper conducts a comparative study of the use of the recent deep learning models (VGG16, VGG19, DenseNet121, Inception-ResNet-V2, InceptionV3, Resnet50, and Xception) to deal with the detection and classification of coronavirus pneumonia from pneumonia cases. This study uses 7165 chest X-ray images of COVID-19 (1536) and pneumonia (5629) patients. Confusion metrics and performance metrics were used to analyze each model. Results show DenseNet121 (99.48% of accuracy) showed better performance when compared with the other models in this study.

Item Type: Article
Funders: 2020 EBC-C (Extra-Budgetary Contributions from China) Project on Promoting the Use of ICT for Achievement of Sustainable Development Goals (IF015-2021), University of Malaya
Uncontrolled Keywords: Artificial neural networks; Deep learning; Transfer learning; Multi-task learning; COVID-19; Classification; DenseNet121
Subjects: R Medicine > R Medicine (General)
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering
Faculty of Engineering > Department of Electrical Engineering
Depositing User: Ms Zaharah Ramly
Date Deposited: 05 Aug 2022 07:58
Last Modified: 05 Aug 2022 07:58
URI: http://eprints.um.edu.my/id/eprint/28205

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