基于深度学习卷积神经网络的局部ct扫描图像Covid-19检测

S. Widodo, Anik Sulistiyanti, Indra Agung Yudistira
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摘要

2019年肺炎冠状病毒病(COVID-19)是由严重急性呼吸综合征冠状病毒2 (SARS-CoV-2)引起的肺实质炎症。为确定Covid-19诊断而进行的辅助检查是通过放射检查,其中一种是计算机断层扫描(CT-Scan)。目前从CT扫描图像诊断新冠肺炎的方法是通过肉眼研究二维CT扫描图像数据集,然后逐一解释数据。这个程序无效。研究目标是利用深度学习卷积神经网络(CNN)开发在局部ct扫描图像上检测新冠病毒的应用。使用的预训练模型为ResNet-50。试验进行了3次。第一次测试使用原始CT图像,准确率为92.5%。第二个测试使用与周围组织分离的肺部CT数据,准确率为95%。最后一次测试使用局部CT数据(Covid Candidate),获得的准确率为98%。
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Detection of Covid-19 on Localized Ct-scan Images Using Deep Learning Convolution Neural Network
Pneumonia Coronavirus Disease 2019 (COVID-19) is an inflammation of the lung parenchyma caused by Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Supporting examinations carried out to establish a diagnosis of Covid-19 is through radiological examinations, one of which is Computed Tomography Scan (CT-Scan). The current method used to diagnose COVID-19 from CT scan images is by studying the 2-D CT Scan image data set using naked eye, then interpreting the data one by one. This procedure is ineffective. Research aim is to develop Covid-19 detection application on localized CT-Scan images using Deep Learning Convolution Neural Network (CNN). Pre-Trained Model used is ResNet-50. Test was carried out 3 times. First test uses original CT image, with an accuracy of 92.5%. Second test used pulmonary CT data that had been separated from surrounding tissue, with an accuracy of 95%. The last test used localized CT data (Covid Candidate), the accuracy obtained was 98%.
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