Abstract

The COVID-19 outbreak began in December 2019 and has dreadfully affected our lives since then. More than three million lives have been engulfed by this newest member of the corona virus family. With the emergence of continuously mutating variants of this virus, it is still indispensable to successfully diagnose the virus at early stages. Although the primary technique for the diagnosis is the PCR test, the non-contact methods utilizing the chest radiographs and CT scans are always preferred. Artificial intelligence, in this regard, plays an essential role in the early and accurate detection of COVID-19 using pulmonary images. In this research, a transfer learning technique with fine tuning was utilized for the detection and classification of COVID-19. Four pre-trained models i.e., VGG16, DenseNet-121, ResNet-50, and MobileNet were used. The aforementioned deep neural networks were trained using the dataset (available on Kaggle) of 7232 (COVID-19 and normal) chest X-ray images. An indigenous dataset of 450 chest X-ray images of Pakistani patients was collected and used for testing and prediction purposes. Various important parameters, e.g., recall, specificity, F1-score, precision, loss graphs, and confusion matrices were calculated to validate the accuracy of the models. The achieved accuracies of VGG16, ResNet-50, DenseNet-121, and MobileNet are 83.27%, 92.48%, 96.49%, and 96.48%, respectively. In order to display feature maps that depict the decomposition process of an input image into various filters, a visualization of the intermediate activations is performed. Finally, the Grad-CAM technique was applied to create class-specific heatmap images in order to highlight the features extracted in the X-ray images. Various optimizers were used for error minimization purposes. DenseNet-121 outperformed the other three models in terms of both accuracy and prediction.

Highlights

  • The world has been going through an existential pandemic of the COVID-19 disease since December 2019, and it has affected every aspect of our lives [1]

  • The training of the four models in this study was performed on the augmented data

  • The dataset images were resized to 224 × 224 pixels

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Summary

Introduction

The world has been going through an existential pandemic of the COVID-19 disease since December 2019, and it has affected every aspect of our lives [1]. This pandemic has had an intense social and economic impact on most countries. COVID-19 is characterized by a high transmissibility and has a significant mortality rate [2]. More than 200 countries and territories have been reported to become infected by this virus. As of 29 June 2021, nearly 180 million confirmed cases of COVID-19 and four million deaths have been reported globally [3]

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