COVID-19 Detection from Computed Tomography Images Using Slice Processing Techniques and a Modified Xception Classifier.

IF 3.3 Q2 ENGINEERING, BIOMEDICAL
International Journal of Biomedical Imaging Pub Date : 2024-05-24 eCollection Date: 2024-01-01 DOI:10.1155/2024/9962839
Kenan Morani, Esra Kaya Ayana, Dimitrios Kollias, Devrim Unay
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引用次数: 0

Abstract

This paper extends our previous method for COVID-19 diagnosis, proposing an enhanced solution for detecting COVID-19 from computed tomography (CT) images using a lean transfer learning-based model. To decrease model misclassifications, two key steps of image processing were employed. Firstly, the uppermost and lowermost slices were removed, preserving sixty percent of each patient's slices. Secondly, all slices underwent manual cropping to emphasize the lung areas. Subsequently, resized CT scans (224 × 224) were input into an Xception transfer learning model with a modified output. Both Xception's architecture and pretrained weights were leveraged in the method. A big and rigorously annotated database of CT images was used to verify the method. The number of patients/subjects in the dataset is more than 5000, and the number and shape of the slices in each CT scan varies greatly. Verification was made both on the validation partition and on the test partition of unseen images. Results on the COV19-CT database showcased not only improvement from our previous solution and the baseline but also comparable performance to the highest-achieving methods on the same dataset. Further validation studies could explore the scalability and adaptability of the developed methodologies across diverse healthcare settings and patient populations. Additionally, investigating the integration of advanced image processing techniques, such as automated region of interest detection and segmentation algorithms, could enhance the efficiency and accuracy of COVID-19 diagnosis.

利用切片处理技术和改进的 Xception 分类器从计算机断层扫描图像中检测 COVID-19。
本文扩展了之前的 COVID-19 诊断方法,提出了一种基于精益迁移学习模型的增强型解决方案,用于从计算机断层扫描(CT)图像中检测 COVID-19。为了减少模型分类错误,我们采用了两个关键的图像处理步骤。首先,去除最上方和最下方的切片,保留每位患者 60% 的切片。其次,对所有切片进行手动裁剪,以突出肺部区域。随后,将调整后的 CT 扫描图像(224 × 224)输入 Xception 转移学习模型,并修改输出结果。该方法利用了 Xception 的架构和预训练权重。为了验证该方法,我们使用了一个大型且经过严格注释的 CT 图像数据库。数据集中的患者/受试者数量超过 5000 人,且每张 CT 扫描图像的切片数量和形状差异很大。验证既在验证分区上进行,也在未见图像的测试分区上进行。在 COV19-CT 数据库上的结果表明,该方法不仅比我们以前的解决方案和基线方法有所改进,而且在同一数据集上的性能也可与成绩最好的方法媲美。进一步的验证研究可以探索所开发方法在不同医疗环境和患者群体中的可扩展性和适应性。此外,研究先进的图像处理技术(如自动兴趣区检测和分割算法)的整合也能提高 COVID-19 诊断的效率和准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
12.00
自引率
0.00%
发文量
11
审稿时长
20 weeks
期刊介绍: The International Journal of Biomedical Imaging is managed by a board of editors comprising internationally renowned active researchers. The journal is freely accessible online and also offered for purchase in print format. It employs a web-based review system to ensure swift turnaround times while maintaining high standards. In addition to regular issues, special issues are organized by guest editors. The subject areas covered include (but are not limited to): Digital radiography and tomosynthesis X-ray computed tomography (CT) Magnetic resonance imaging (MRI) Single photon emission computed tomography (SPECT) Positron emission tomography (PET) Ultrasound imaging Diffuse optical tomography, coherence, fluorescence, bioluminescence tomography, impedance tomography Neutron imaging for biomedical applications Magnetic and optical spectroscopy, and optical biopsy Optical, electron, scanning tunneling/atomic force microscopy Small animal imaging Functional, cellular, and molecular imaging Imaging assays for screening and molecular analysis Microarray image analysis and bioinformatics Emerging biomedical imaging techniques Imaging modality fusion Biomedical imaging instrumentation Biomedical image processing, pattern recognition, and analysis Biomedical image visualization, compression, transmission, and storage Imaging and modeling related to systems biology and systems biomedicine Applied mathematics, applied physics, and chemistry related to biomedical imaging Grid-enabling technology for biomedical imaging and informatics
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