Rani Geeta, Misra Ankit, Dhaka Vijaypal Singh, Buddhi Deepak, Sharma Ravindra Kumar, Zumpano Ester, Vocaturo Eugenio
Manipal University Jaipur, India.
R.G. Stone Urology and Laparoscopy Hospital, India.
Intell Syst Appl. 2022 Nov;16:200148. doi: 10.1016/j.iswa.2022.200148. Epub 2022 Nov 7.
The high transmission rate of COVID-19 and the lack of quick, robust, and intelligent systems for its detection have become a point of concern for the public, Government, and health experts worldwide. The study of radiological images is one of the fastest ways to comprehend the infectious spread and diagnose a patient. However, it is difficult to differentiate COVID-19 from other pneumonic infections. The purpose of this research is to provide an automatic, precise, reliable, robust, and intelligent assisting system 'Covid Scanner' for mass screening of COVID-19, Non-COVID Viral Pneumonia, and Bacterial Pneumonia from healthy chest radiographs. To train the proposed system, the authors of this research prepared novel a dataset called, "COVID-Pneumonia CXR". The system is a coherent integration of bone suppression, lung segmentation, and the proposed classifier, 'EXP-Net'. The system reported an AUC of 96.58% on the validation dataset and 96.48% on the testing dataset comprising chest radiographs. The results from the ablation study prove the efficacy and generalizability of the proposed integrated pipeline of models. To prove the system's reliability, the feature heatmaps visualized in the lung region were validated by radiology experts. Moreover, a comparison with the state-of-the-art models and existing approaches shows that the proposed system finds clearer demarcation between the highly similar chest radiographs of COVID-19 and Non-COVID viral pneumonia. The copyright of "Covid Scanner" is protected with registration number SW-13625/2020. The code for the models used in this research is publicly available at: https://github.com/Ankit-Misra/multi_modal_covid_detection/.
新型冠状病毒肺炎（COVID-19）的高传播率以及缺乏快速、强大且智能的检测系统已成为全球公众、政府和健康专家关注的焦点。对放射影像的研究是了解感染传播情况和诊断患者的最快方法之一。然而，很难将COVID-19与其他肺部感染区分开来。本研究的目的是提供一个自动、精确、可靠、强大且智能的辅助系统“新冠扫描仪”，用于从健康胸部X光片中大规模筛查COVID-19、非COVID病毒性肺炎和细菌性肺炎。为了训练所提出的系统，本研究的作者准备了一个名为“COVID-肺炎CXR”的新型数据集。该系统是骨抑制、肺部分割和所提出的分类器“EXP-Net”的连贯集成。该系统在包含胸部X光片的验证数据集上的AUC为96.58%，在测试数据集上为96.48%。消融研究的结果证明了所提出的集成模型管道的有效性和通用性。为了证明该系统的可靠性，肺区域可视化的特征热图由放射学专家进行了验证。此外，与现有最先进模型和现有方法的比较表明，所提出的系统在COVID-19和非COVID病毒性肺炎高度相似的胸部X光片之间找到了更清晰的界限。“新冠扫描仪”的版权受到注册号SW-13625/2020的保护。本研究中使用的模型代码可在以下网址公开获取：https://github.com/Ankit-Misra/multi_modal_covid_detection/
