Jain Rachna, Gupta Meenu, Taneja Soham, Hemanth D Jude
Department of CSE, Bharati Vidyapeeth's College of Engineering, Delhi, India.
Department of CSE, Chandigarh University, Punjab, India.
Appl Intell (Dordr). 2021;51(3):1690-1700. doi: 10.1007/s10489-020-01902-1. Epub 2020 Oct 9.
Covid-19 is a rapidly spreading viral disease that infects not only humans, but animals are also infected because of this disease. The daily life of human beings, their health, and the economy of a country are affected due to this deadly viral disease. Covid-19 is a common spreading disease, and till now, not a single country can prepare a vaccine for COVID-19. A clinical study of COVID-19 infected patients has shown that these types of patients are mostly infected from a lung infection after coming in contact with this disease. Chest x-ray (i.e., radiography) and chest CT are a more effective imaging technique for diagnosing lunge related problems. Still, a substantial chest x-ray is a lower cost process in comparison to chest CT. Deep learning is the most successful technique of machine learning, which provides useful analysis to study a large amount of chest x-ray images that can critically impact on screening of Covid-19. In this work, we have taken the PA view of chest x-ray scans for covid-19 affected patients as well as healthy patients. After cleaning up the images and applying data augmentation, we have used deep learning-based CNN models and compared their performance. We have compared Inception V3, Xception, and ResNeXt models and examined their accuracy. To analyze the model performance, 6432 chest x-ray scans samples have been collected from the Kaggle repository, out of which 5467 were used for training and 965 for validation. In result analysis, the Xception model gives the highest accuracy (i.e., 97.97%) for detecting Chest X-rays images as compared to other models. This work only focuses on possible methods of classifying covid-19 infected patients and does not claim any medical accuracy.
新冠病毒病是一种迅速传播的病毒性疾病，不仅感染人类，动物也会因此患病。这种致命的病毒性疾病影响着人类的日常生活、健康以及一个国家的经济。新冠病毒病是一种常见的传播性疾病，到目前为止，没有一个国家能够研制出针对新冠病毒病的疫苗。对新冠病毒病感染患者的临床研究表明，这类患者在接触该疾病后大多因肺部感染而患病。胸部X光（即放射摄影）和胸部CT是诊断肺部相关问题更有效的成像技术。不过，与胸部CT相比，普通的胸部X光检查成本更低。深度学习是机器学习中最成功的技术，它能对大量胸部X光图像进行有用的分析，这对新冠病毒病的筛查可能会产生重大影响。在这项工作中，我们获取了新冠病毒病感染患者以及健康患者的胸部X光扫描的后前位视图。在对图像进行清理并应用数据增强后，我们使用了基于深度学习的卷积神经网络（CNN）模型，并比较了它们的性能。我们比较了Inception V3、Xception和ResNeXt模型，并检验了它们的准确率。为了分析模型性能，我们从Kaggle数据库中收集了6432份胸部X光扫描样本，其中5467份用于训练，965份用于验证。在结果分析中，与其他模型相比，Xception模型在检测胸部X光图像时的准确率最高（即97.97%）。这项工作仅关注对新冠病毒病感染患者进行分类的可能方法，并不声称具有任何医学准确性。
