Automatic detection of COVID-19 from chest x-ray images using EfficientNet-B7 CNN model with channel-wise attention
Open accessAbstract
Since the outbreak of the global COVID-19 pandemic in Wuhan, China, in 2019, its impact has been seen worldwide.Early identification of COVID-19 is very crucial, as it keeps the infected people isolated from other people, thus minimizing the risk of further transmission.The standard diagnostic approach is based on RT-PCR.However, due to the scarcity of PCR kits in some regions and the costs associated with this technique, there is a growing demand for alternative solutions.Recently, diagnosis of COVID-19 by medical imaging has been recognized as a valid clinical practice.Meanwhile, the massive increase in COVID-19 cases has put considerable pressure on radiologists responsible for interpreting these scans.This paper introduces an automated detection approach as a rapid alternative for COVID-19 diagnosis.We present a deep CNN model to differentiate between normal and pneumonia cases, as well as patients with COVID-19.Our approach is based on EfficientNet-B7 architecture and improved with Squeeze and Excitation block as an attention mechanism.In addition, we propose an innovative architecture that combines CNN with SVM to achieve the best performance.Experimental results show that the proposed framework provides better performance than existing SOTA methods, with an average accuracy of 97.50%, while the precision and recall of COVID-19 are both 100%.
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The high volume of chest x-rays, combined with the need for detailed narrative reporting, places significant pressure on radiologists, especially during night shifts and peak admission periods.
“The high volume of chest x-rays, combined with the need for detailed narrative reporting, places significant pressure on radiologists, especially during night shifts and peak admission periods (9, 10).”
From Swin-Qwen3: a three-stage vision–language framework for automated radiology report generation with multi-agent verification (2026) · cites this paper as [9]
The Introduction (page not provided) says the rapid spread of COVID-19 imposed a significant workload on radiologists and discusses faster interpretation of chest radiographs. It does not attribute that workload to detailed narrative reporting or identify night shifts and peak admission periods as especially demanding. (LLM, conf=0.88)
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- Authors
- Rami Naidji, M., & Elberrichi, Z.
- Affiliations
- Université Djilali de Sidi Bel Abbès (DZ)
- ORCID
- Elberrichi, Zakaria
- Venue
- International Journal of Computing and Digital Systems
- Publisher
- Deanship of Scientific Research
- Published
- 2024-03-15
- Volume
- 15
- Issue
- 1
- Pages
- 1443-1456
- ISSN
- 2210-142X (ISSN-L)
- OpenAlex
- W4392805431
- Cited by
- 4
- References
- 61
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 1
- FWCI
- 1.02
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- International Journal of Computing and Digital Systems (publishedVersion, cc-by-nc-nd)
- 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, chest X-ray images, EfficientNetB7, squeeze-and-excitation, channel attention, pneumonia classification, automated medical diagnosis, deep learning
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