Al-Antari Mugahed A, Hua Cam-Hao, Bang Jaehun, Lee Sungyoung
Department of Computer Science and Engineering, College of Software, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104 Republic of Korea.
Department of Biomedical Engineering, Sana'a Community College, Sana'a, Republic of Yemen.
Appl Intell (Dordr). 2021;51(5):2890-2907. doi: 10.1007/s10489-020-02076-6. Epub 2020 Nov 28.
Coronavirus disease 2019 (COVID-19) is a novel harmful respiratory disease that has rapidly spread worldwide. At the end of 2019, COVID-19 emerged as a previously unknown respiratory disease in Wuhan, Hubei Province, China. The world health organization (WHO) declared the coronavirus outbreak a pandemic in the second week of March 2020. Simultaneous deep learning detection and classification of COVID-19 based on the full resolution of digital X-ray images is the key to efficiently assisting patients by enabling physicians to reach a fast and accurate diagnosis decision. In this paper, a simultaneous deep learning computer-aided diagnosis (CAD) system based on the YOLO predictor is proposed that can detect and diagnose COVID-19, differentiating it from eight other respiratory diseases: atelectasis, infiltration, pneumothorax, masses, effusion, pneumonia, cardiomegaly, and nodules. The proposed CAD system was assessed via five-fold tests for the multi-class prediction problem using two different databases of chest X-ray images: COVID-19 and ChestX-ray8. The proposed CAD system was trained with an annotated training set of 50,490 chest X-ray images. The regions on the entire X-ray images with lesions suspected of being due to COVID-19 were simultaneously detected and classified end-to-end via the proposed CAD predictor, achieving overall detection and classification accuracies of 96.31% and 97.40%, respectively. Most test images from patients with confirmed COVID-19 and other respiratory diseases were correctly predicted, achieving average intersection over union (IoU) greater than 90%. Applying deep learning regularizers of data balancing and augmentation improved the COVID-19 diagnostic performance by 6.64% and 12.17% in terms of the overall accuracy and the F1-score, respectively. It is feasible to achieve a diagnosis based on individual chest X-ray images with the proposed CAD system within 0.0093 s. Thus, the CAD system presented in this paper can make a prediction at the rate of 108 frames/s (FPS), which is close to real-time. The proposed deep learning CAD system can reliably differentiate COVID-19 from other respiratory diseases. The proposed deep learning model seems to be a reliable tool that can be used to practically assist health care systems, patients, and physicians.
2019冠状病毒病（COVID-19）是一种新型有害呼吸道疾病，已在全球迅速传播。2019年底，COVID-19在中国湖北省武汉市作为一种此前未知的呼吸道疾病出现。世界卫生组织（WHO）于2020年3月的第二周宣布冠状病毒爆发为大流行病。基于数字X射线图像的全分辨率对COVID-19进行同步深度学习检测和分类，是通过使医生能够快速准确地做出诊断决定来有效协助患者的关键。本文提出了一种基于YOLO预测器的同步深度学习计算机辅助诊断（CAD）系统，该系统可以检测和诊断COVID-19，并将其与其他八种呼吸道疾病区分开来：肺不张、浸润、气胸、肿块、胸腔积液、肺炎、心脏肥大和结节。使用两个不同的胸部X射线图像数据库（COVID-19和ChestX-ray8），通过五重测试对提出的CAD系统进行了多类预测问题的评估。提出的CAD系统使用50490张胸部X射线图像的带注释训练集进行训练。通过提出的CAD预测器对整个X射线图像上疑似由COVID-19引起病变的区域进行端到端的同步检测和分类，总体检测准确率和分类准确率分别达到96.31%和97.40%。大多数来自确诊COVID-19患者和其他呼吸道疾病患者的测试图像都被正确预测，平均交并比（IoU）大于90%。应用数据平衡和增强的深度学习正则化方法，在总体准确率和F1分数方面，分别将COVID-19的诊断性能提高了6.64%和12.17%。使用提出的CAD系统在0.0093秒内基于单个胸部X射线图像进行诊断是可行的。因此，本文提出的CAD系统可以以108帧/秒（FPS）的速度进行预测，接近实时。提出的深度学习CAD系统可以可靠地将COVID-19与其他呼吸道疾病区分开来。提出的深度学习模型似乎是一种可靠的工具，可用于实际协助医疗保健系统、患者和医生。
