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Can AI help in screening Viral and COVID-19 pneumonia?
Muhammad E H Chowdhury.
Tawsifur Rahman.
Amith Khandakar.
Rashid Mazhar.
Muhammad Abdul Kadir.
Zaid Bin Mahbub.
Khandakar R Islam.
Muhammad Salman Khan.
Atif Iqbal.
Nasser Al-Emadi.
Mamun Bin Ibne Reaz.
Acceso Abierto
Coronavirus disease (COVID-19) is a pandemic disease, which has already infected more than half a million people and caused fatalities of above 30 thousand. The aim of this paper is to automatically detect COVID-19 pneumonia patients using digital x-ray images while maximizing the accuracy in detection using image pre-processing and deep-learning techniques. A public database was created by the authors using three public databases and also by collecting images from recently published articles. The database contains a mixture of 190 COVID-19, 1345 viral pneumonia, and 1341 normal chest x-ray images. An image augmented training set was created with 2500 images of each category for training and validating four different pre-trained deep Convolutional Neural Networks (CNNs). These networks were tested for the classification of two different schemes (normal and COVID-19 pneumonia; normal, viral and COVID-19 pneumonia). The classification accuracy, sensitivity, specificity and precision for both the schemes were 98.3%, 96.7%, 100%, 100% and 98.3%, 96.7%, 99%, 100%, respectively. The high accuracy of this computer-aided diagnostic tool can significantly improve the speed and accuracy of diagnosing cases with COVID-19. This would be highly useful in this pandemic where disease burden and need for preventive measures are at odds with available resources.
Appears in Collections:Artículos científicos

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