ModCOVNN: a convolutional neural network approach in COVID-19 prognosis

(1) * Ahmed Wasif Reza Mail (Department of Computer Science and Engineering, East West University, Bangladesh)
(2) Jannatul Ferdous Sorna Mail (Department of Computer Science and Engineering, East West University, Bangladesh)
(3) Md. Momtaz Uddin Rashel Mail (Department of Computer Science and Engineering, East West University, Bangladesh)
(4) Mir Moynuddin Ahmed Shibly Mail (Department of Computer Science and Engineering, East West University, Bangladesh)
*corresponding author

Abstract


COVID-19 is a devastating pandemic in the history of humankind. It is a highly contagious flu that can spread from human to human. For being so contagious, detecting patients with it and isolating them has become the primary concern for healthcare professionals. However, identifying COVID-19 patients with a Polymerase chain reaction (PCR) test can sometimes be problematic and time-consuming. Therefore, detecting patients with this virus from X-ray chest images can be a perfect alternative to the de-facto standard PCR test. This article aims at providing such a decision support system that can detect COVID-19 patients with the help of X-ray images. To do that, a novel convolutional neural network (CNN) based architecture, namely ModCOVNN, has been introduced. To determine whether the proposed model works with good efficiency, two CNN-based architectures – VGG16 and VGG19 have been developed for the detection task. The experimental results of this study have proved that the proposed architecture has outperformed the other two models with 98.08% accuracy, 98.14% precision, and 98.4% recall. This result indicates that proper detection of COVID-19 patients with the help of X-ray images of the chest is possible using machine learning methods with high accuracy. This type of data-driven system can help us to overcome the current appalling situation throughout the world.

Keywords


COVID19; convolutional neural network; biomedical image processing; computer vision; X-ray

   

DOI

https://doi.org/10.26555/ijain.v7i2.604
      

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