Detection and Severity Classification of COVID-19 in CT Images Using Deep Learning.

Yazan Qiblawey,Somaya Al Maadeed,Sakib Mahmud,Tawsifur Rahman,Amith Khandakar,Mohamed Arselene Ayari,Serkan Kiranyaz,Farayi Musharavati,Nabil Ibtehaz,Anas Tahir,Muhammad E H Chowdhury

doi:10.3390/diagnostics11050893

Abstract

Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. In this study, a cascaded system is proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography images. An extensive set of experiments were performed using Encoder–Decoder Convolutional Neural Networks (ED-CNNs), UNet, and Feature Pyramid Network (FPN), with different backbone (encoder) structures using the variants of DenseNet and ResNet. The conducted experiments for lung region segmentation showed a Dice Similarity Coefficient (DSC) of 97.19% and Intersection over Union (IoU) of 95.10% using U-Net model with the DenseNet 161 encoder. Furthermore, the proposed system achieved an elegant performance for COVID-19 infection segmentation with a DSC of 94.13% and IoU of 91.85% using the FPN with DenseNet201 encoder. The proposed system can reliably localize infections of various shapes and sizes, especially small infection regions, which are rarely considered in recent studies. Moreover, the proposed system achieved high COVID-19 detection performance with 99.64% sensitivity and 98.72% specificity. Finally, the system was able to discriminate between different severity levels of COVID-19 infection over a dataset of 1110 subjects with sensitivity values of 98.3%, 71.2%, 77.8%, and 100% for mild, moderate, severe, and critical, respectively.

Highlights

Introduction published maps and institutional affilThe coronavirus disease 2019 (COVID-19) has become a global pandemic, which affects different aspects of human life
The proposed system consists of three main stages as shown in Figure 1, where the segmentation of lung from computerized tomography (CT) images is the first step of our proposed system
This section describes the results of the lung and lesion segmentation, COVID-19 detection and severity classification, along with 3D lung modeling to visualize lung infections

Summary

Introduction

The coronavirus disease 2019 (COVID-19) has become a global pandemic, which affects different aspects of human life. Until 11 January 2020, more than 88.8 million confirmed cases and 1.92 million death cases have been recorded and its infection rate is still rapidly increasing worldwide [1]. Several laboratory identification tools are used for the detection of COVID-19, such as real-time reverse transcription-polymerase chain reaction (RT-PCR). Isothermal nucleic acid amplification technology [2,3]. RT-PCR is considered the gold standard to detect COVID-19 [4]. A high false alarm rate usually occurs due to the sample contamination, damage, or virus mutations in the COVID-19 genome. Medical imaging can be considered a first-line investigation tool [5]. Several studies [6,7]

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Conclusion