CO-ResNet:优化的基于x射线图像诊断COVID-19的ResNet模型

Subrato Bharati, Prajoy Podder, M. Mondal, V. B. Surya Prasath
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引用次数: 39

摘要

本文主要研究基于深度学习(DL)的模型在新型冠状病毒病(COVID-19) x射线图像分析中的应用。这项工作的新颖之处在于开发了一种新的深度学习算法,称为COVID-19的优化剩余网络(CO-ResNet)。本文提出的CO-ResNet是在传统ResNet 101的基础上进行超参数调优的。CO-ResNet应用于从两个公开可用的数据集中检索的5,935张x射线图像的新数据集。通过调整大小、增强和归一化以及不同时期的测试,优化了我们的CO-ResNet用于检测COVID-19与正常健康肺对照的肺炎。使用了不同的评价指标,如分类准确率、F1分数、召回率、精度、接收者工作特征曲线下面积(AUC)。我们提出的CO-ResNet在包括健康肺、肺炎影响肺和COVID-19影响肺样本在内的多层次数据分类问题中获得了一致的最佳性能。在实验评估中,以ResNet101为骨干的CO-ResNet对COVID-19的检出率准确率为98.74%,对健康正常肺、肺炎感染肺的检出率准确率分别为92.08%和91.32%。此外,我们的模型对ResNet152骨干网的健康正常肺和肺炎感染肺的准确率分别为83.68%和82%。实验结果表明我们的新DL驱动模型在COVID-19和肺炎分类中的潜在用途。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CO-ResNet: Optimized ResNet model for COVID-19 diagnosis from X-ray images
This paper focuses on the application of deep learning (DL) based model in the analysis of novel coronavirus disease (COVID-19) from X-ray images. The novelty of this work is in the development of a new DL algorithm termed as optimized residual network (CO-ResNet) for COVID-19. The proposed CO-ResNet is developed by applying hyperparameter tuning to the conventional ResNet 101. CO-ResNet is applied to a novel dataset of 5,935 X-ray images retrieved from two publicly available datasets. By utilizing resizing, augmentation and normalization and testing different epochs our CO-ResNet was optimized for detecting COVID-19 versus pneumonia with normal healthy lung controls. Different evaluation metrics such as the classification accuracy, F1 score, recall, precision, area under the receiver operating characteristics curve (AUC) are used. Our proposed CO-ResNet obtains consistently best performance in the multi-level data classification problem, including health lung, pneumonia affected lung and COVID-19 affected lung samples. In the experimental evaluation, the detection rate accuracy in discerning COVID-19 is 98.74%, and for healthy normal lungs, pneumonia affected lungs are 92.08% and 91.32% respectively for our CO-ResNet with ResNet101 backbone. Further, our model obtained accuracy values of 83.68% and 82% for healthy normal lungs and pneumonia affected lungs with ResNet152 backbone. Experimental results indicate the potential usage of our new DL driven model for classification of COVID-19 and pneumonia.
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