A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2
Open accessAbstract
In this paper, we have trained several deep convolutional networks with introduced training techniques for classifying X-ray images into three classes: normal, pneumonia, and COVID-19, based on two open-source datasets. Our data contains 180 X-ray images that belong to persons infected with COVID-19, and we attempted to apply methods to achieve the best possible results. In this research, we introduce some training techniques that help the network learn better when we have an unbalanced dataset (fewer cases of COVID-19 along with more cases from other classes). We also propose a neural network that is a concatenation of the Xception and ResNet50V2 networks. This network achieved the best accuracy by utilizing multiple features extracted by two robust networks. For evaluating our network, we have tested it on 11302 images to report the actual accuracy achievable in real circumstances. The average accuracy of the proposed network for detecting COVID-19 cases is 99.50%, and the overall average accuracy for all classes is 91.4%.
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The suggested Xception transformation approach achieves extra learning efficiency and better performance but does not minimize the number of parameters.
“Conversely, the suggested Xception transformation approach achieves extra learning efficiency and better performance but does not minimize the number of parameters [126, 127].”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [127]
The Methodology (Training phase; page unavailable) says the concatenation of Xception and ResNet50V2 improved accuracy and had more parameters than either network alone. It does not establish greater learning efficiency for the Xception approach: the passage attributes increased training efficiency to data augmentation, and the reported performance gain belongs to the concatenated network rather than Xception alone. (LLM, conf=0.84)
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- Authors
- Rahimzadeh, M., & Attar, A.
- Affiliations
- Iran University of Science and Technology (IR)
- Sharif University of Technology (IR)
- Corresponding author
- Rahimzadeh, Mohammad
- ORCID
- Rahimzadeh, Mohammad
- Venue
- Informatics in Medicine Unlocked
- Publisher
- Elsevier BV
- Published
- 2020
- Volume
- 19
- Pages
- 100360
- ISSN
- 2352-9148 (ISSN-L)
- PMC
- PMC7255267
- OpenAlex
- W3017141460
- Cited by
- 695
- References
- 20
- Citations by year
- 2026: 23 · 2025: 63 · 2024: 84 · 2023: 134 · 2022: 170 · 2021: 174 · 2020: 47
- FWCI
- 63.93
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Informatics in Medicine Unlocked (publishedVersion, cc-by-nc-nd)
- arXiv (Cornell University) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- COVID-19 detection, pneumonia detection, chest X-ray images, Xception, ResNet50V2, hybrid convolutional neural network, class imbalance, transfer learning, medical image classification
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