基于计算机断层肺图像深度学习的COVID-19分割分类框架

Wessam M. Salama , Moustafa H. Aly
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引用次数: 12

摘要

2019冠状病毒病(COVID-19)已影响全球数百万人,造成630多万人死亡(世界卫生组织,2022年6月)。人们越来越多地尝试开发基于计算机断层扫描(CT)肺部图像诊断COVID-19的深度学习方法。复制和获得CT肺数据是一个挑战,因为它不是公开的。本文介绍了一种新的广义框架来分割和分类CT图像,并根据肺部CT图像确定患者的COVID-19检测是阳性还是阴性。在这项工作中,探索了许多不同的分类任务策略。采用ResNet50和VGG16模型对CT肺部图像进行COVID-19阳性和阴性分类。并结合深度学习中最常用的图像分割架构之一的U-Net,利用VGG16和rennet50在分类前对CT肺部图像进行分割,提高系统性能。此外,利用图像大小相关归一化技术(ISDNT)和维纳滤波作为预处理技术来增强图像和抑制噪声。此外,通过迁移学习和数据增强技术解决了COVID-19 CT肺部图像不足的问题,避免了深度模型的过拟合。所提出的框架包括端到端、VGG16、ResNet50和带VGG16或ResNet50的U-Net框架,并应用于来自Kaggle的COVID-19肺部CT图像的数据集。分类结果表明,使用预处理后的CT肺图像作为U-Net混合ResNet50算法的输入,分类效果最好。该分类模型的准确率(ACC)为98.98%,ROC曲线下面积(AUC)为98.87%,灵敏度(Se)为98.89%,精度(Pr)为97.99%,F1-得分为97.88%,计算时间为1.8974 s。
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Framework for COVID-19 segmentation and classification based on deep learning of computed tomography lung images

Corona Virus Disease 2019 (COVID-19) has affected millions of people worldwide and caused more than 6.3 million deaths (World Health Organization, June 2022). Increased attempts have been made to develop deep learning methods to diagnose COVID-19 based on computed tomography (CT) lung images. It is a challenge to reproduce and obtain the CT lung data, because it is not publicly available. This paper introduces a new generalized framework to segment and classify CT images and determine whether a patient is tested positive or negative for COVID-19 based on lung CT images. In this work, many different strategies are explored for the classification task. ResNet50 and VGG16 models are applied to classify CT lung images into COVID-19 positive or negative. Also, VGG16 and ReNet50 combined with U-Net, which is one of the most used architectures in deep learning for image segmentation, are employed to segment CT lung images before the classifying process to increase system performance. Moreover, the image size dependent normalization technique (ISDNT) and Wiener filter are utilized as the preprocessing techniques to enhance images and noise suppression. Additionally, transfer learning and data augmentation techniques are performed to solve the problem of COVID-19 CT lung images deficiency, therefore the over-fitting of deep models can be avoided. The proposed frameworks, which comprise of, end-to-end, VGG16, ResNet50, and U-Net with VGG16 or ResNet50, are applied on the dataset which is sourced from COVID-19 lung CT images in Kaggle. The classification results show that using the preprocessed CT lung images as the input for U-Net hybrid with ResNet50 achieves the best performance. The proposed classification model achieves 98.98% accuracy (ACC), 98.87% area under the ROC curve (AUC), 98.89% sensitivity (Se), 97.99% precision (Pr), 97.88% F1- score, and 1.8974-second computational time.

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来源期刊
Journal of Electronic Science and Technology
Journal of Electronic Science and Technology Engineering-Electrical and Electronic Engineering
CiteScore
4.30
自引率
0.00%
发文量
1362
审稿时长
99 days
期刊介绍: JEST (International) covers the state-of-the-art achievements in electronic science and technology, including the most highlight areas: ¨ Communication Technology ¨ Computer Science and Information Technology ¨ Information and Network Security ¨ Bioelectronics and Biomedicine ¨ Neural Networks and Intelligent Systems ¨ Electronic Systems and Array Processing ¨ Optoelectronic and Photonic Technologies ¨ Electronic Materials and Devices ¨ Sensing and Measurement ¨ Signal Processing and Image Processing JEST (International) is dedicated to building an open, high-level academic journal supported by researchers, professionals, and academicians. The Journal has been fully indexed by Ei INSPEC and has published, with great honor, the contributions from more than 20 countries and regions in the world.
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