Covid-19 detection from chest x-ray images: comparison of well-established convolutional neural networks models

(1) * Muhammad Amir As'ari Mail (Institute of Human Centered Engineering (IHCE), Universiti Teknologi Malaysia, Johor Bahru, Malaysia)
(2) Nur Izzaty Ab Manap Mail (School of Biomedical Engineering and Health Sciences, Universiti Teknologi Malaysia, Johor Bahru, Malaysia)
*corresponding author

Abstract


Coronavirus disease 19 (Covid-19) is a pandemic disease that has already killed hundred thousands of people and infected millions more. At the climax disease Covid-19, this virus will lead to pneumonia and result in a fatality in extreme cases. COVID-19 provides radiological cues that can be easily detected using chest X-rays, which distinguishes it from other types of pneumonic disease. Recently, there are several studies using the CNN model only focused on developing binary classifier that classify between Covid-19 and normal chest X-ray. However, no previous studies have ever made a comparison between the performances of some of the established pre-trained CNN models that involving multi-classes including Covid-19, Pneumonia and Normal chest X-ray. Therefore, this study focused on formulating an automated system to detect Covid-19 from chest X-Ray images by four established and powerful CNN models AlexNet, GoogleNet, ResNet-18 and SqueezeNet and the performance of each of the models were compared. A total of 21,252 chest X-ray images from various sources were pre-processed and trained for the transfer learning-based classification task, which included Covid-19, bacterial pneumonia, viral pneumonia, and normal chest x-ray images. In conclusion, this study revealed that all models successfully classify Covid-19 and other pneumonia at an accuracy of more than 78.5%, and the test results revealed that GoogleNet outperforms other models for achieved accuracy of 91.0%, precision of 85.6%, sensitivity of 85.3%, and F1 score of 85.4%.

   

DOI

https://doi.org/10.26555/ijain.v8i2.807
      

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