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

Diagnostics

Nabil Ibtehaz¹  SomayaAl Maadeed²  Serkan Kiranyaz³  MuhammadE. H. Chowdhury³  Anas Tahir³  Yazan Qiblawey³  Amith Khandakar³  Tawsifur Rahman³  Sakib Mahmud³  Farayi Musharavati⁴  MohamedArselene Ayari⁵ 

[1] Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh;Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar;Department of Electrical Engineering, Qatar University, Doha 2713, Qatar;Mechanical & Industrial Engineering Department, Qatar University, Doha 2713, Qatar;Technology Innovation and Engineering Education (TIEE), College of Engineering, Qatar University, Doha 2713, Qatar;

关键词: COVID-19;    lung segmentation;    lesion segmentation;    severity classification;    deep learning;   

DOI  :  10.3390/diagnostics11050893

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown