Aktas Kadir, Ignjatovic Vuk, Ilic Dragan, Marjanovic Marina, Anbarjafari Gholamreza
iCV Research Lab, Institute of Technology, University of Tartu, 51009 Tartu, Estonia.
iVCV OÜ, 51011 Tartu, Estonia.
Signal Image Video Process. 2023;17(4):1035-1041. doi: 10.1007/s11760-022-02309-w. Epub 2022 Jul 20.
One of the main challenges in the current pandemic is the detection of coronavirus. Conventional techniques (PT-PCR) have their limitations such as long response time and limited accessibility. On the other hand, X-ray machines are widely available and they are already digitized in the health systems. Thus, their usage is faster and more available. Therefore, in this research, we evaluate how well deep CNNs do when it comes to classifying normal versus pathological chest X-rays. Compared to the previous research, we trained our network on the largest number of images, 103,468 in total, including 5 classes such as COPD signs, COVID, normal, others and Pneumonia. We achieved COVID accuracy of 97% and overall accuracy of 81%. Additionally, we achieved classification accuracy of 84% for categorization into normal (78%) and abnormal (88%).
当前疫情中的主要挑战之一是冠状病毒的检测。传统技术（PT-PCR）存在局限性，比如响应时间长且可及性有限。另一方面，X射线机广泛可用，并且在医疗系统中已经数字化。因此，其使用更快且更易获得。所以，在本研究中，我们评估深度卷积神经网络在对正常与病理性胸部X光片进行分类时的表现。与之前的研究相比，我们在数量最多的图像上训练了我们的网络，总共103468张，包括慢性阻塞性肺疾病体征、新冠、正常、其他以及肺炎等5类。我们实现了97%的新冠检测准确率以及81%的总体准确率。此外，对于分为正常（78%）和异常（88%）的分类，我们实现了84%的分类准确率。
