Citation

Musha, Ahmmad and Al Mamun, Abdullah Sarwar and Tahabilder, Anik and Hossen, Md. Jakir and Jahan, Busrat and Ranjbari, Sima (2022) A deep learning approach for COVID-19 and pneumonia detection from chest X-ray images. International Journal of Electrical and Computer Engineering (IJECE), 12 (4). p. 3655. ISSN 2088-8708

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Abstract

There has been a surge in biomedical imaging technologies with the recent advancement of deep learning. It is being used for diagnosis from X-ray, computed tomography (CT) scan, electrocardiogram (ECG), and electroencephalography (EEG) images. However, most of them are solely for particular disease detection. In this research, a computer-aided deep learning model named COVID-CXDNetV2 has been presented to detect two separate diseases, coronavirus disease 2019 (COVID-19) and pneumonia, from the X-ray images in real-time. The proposed model is made based on you only look once (YOLOv2) with residual neural network (ResNet) and trained by a vast X-ray images dataset containing 3788 samples of three classes named COVID-19 pneumonia and normal. The model has obtained the maximum overall classification accuracy of 97.9% with a loss of 0.052 for multiclass classification (COVID-19, pneumonia, and normal) and 99.8% accuracy, 99.52% sensitivity, 100% specificity with a loss of 0.001 for binary classification (COVID-19 and normal), which beats some current state-of-the-art results. Authors believe that this method will be applicable in the medical domain for the diagnosis and will significantly contribute to real life.

Item Type:	Article
Uncontrolled Keywords:	Coronavirus, COVID-19, deep learning, pneumonia, X-ray images
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Nov 2022 01:45
Last Modified:	03 Nov 2022 01:45
URII:	http://shdl.mmu.edu.my/id/eprint/10214

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