Noninvasive COVID-19 Screening Using Deep-Learning-Based Multilevel Fusion Model With an Attention Mechanism
The current pandemic has necessitated rapid and automatic detection of coronavirus disease (COVID-19) infections. Various artificial intelligence functionalities coupled with biomedical images can be utilized to efficiently detect these infections and recommend a prompt response (curative interventi...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Open Journal of Instrumentation and Measurement |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10226595/ |
| _version_ | 1797197398748954624 |
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| author | M. Shamim Hossain Mohammad Shorfuzzaman |
| author_facet | M. Shamim Hossain Mohammad Shorfuzzaman |
| author_sort | M. Shamim Hossain |
| collection | DOAJ |
| description | The current pandemic has necessitated rapid and automatic detection of coronavirus disease (COVID-19) infections. Various artificial intelligence functionalities coupled with biomedical images can be utilized to efficiently detect these infections and recommend a prompt response (curative intervention) to limit the virus’s spread. In particular, biomedical imaging could help to visualize the internal organs of the human body and disorders that affect them. One of them is chest X-rays (CXRs) which has widely been used for preventive medicine or disease screening. However, when it comes to detecting COVID-19 from CXR images, most of the approaches rely on standard image classification algorithms, which have limitations with low identification accuracy and improper extraction of key features. As a result, a convolutional neural network (CNN)-based fusion network has been developed for automated COVID-19 screening in this study. First, using attention networks and multiple fine-tuned CNN models, we extract key features that are resistant to overfitting. We then employ a locally connected layer to create a weighted combination of these models for final COVID-19 detection. Using a publicly available dataset of CXR images from healthy subjects as well as COVID-19 and pneumonia cases, we evaluated the predictive capabilities of our proposed model. Test results demonstrate that the proposed fusion model performs favorably compared to individual CNN models. |
| first_indexed | 2024-04-24T06:43:20Z |
| format | Article |
| id | doaj.art-814494c83cb14659935868df5920912b |
| institution | Directory Open Access Journal |
| issn | 2768-7236 |
| language | English |
| last_indexed | 2024-04-24T06:43:20Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Instrumentation and Measurement |
| spelling | doaj.art-814494c83cb14659935868df5920912b2024-04-22T20:23:13ZengIEEEIEEE Open Journal of Instrumentation and Measurement2768-72362023-01-01211210.1109/OJIM.2023.330394410226595Noninvasive COVID-19 Screening Using Deep-Learning-Based Multilevel Fusion Model With an Attention MechanismM. Shamim Hossain0https://orcid.org/0000-0001-5906-9422Mohammad Shorfuzzaman1https://orcid.org/0000-0002-8050-8431Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi ArabiaThe current pandemic has necessitated rapid and automatic detection of coronavirus disease (COVID-19) infections. Various artificial intelligence functionalities coupled with biomedical images can be utilized to efficiently detect these infections and recommend a prompt response (curative intervention) to limit the virus’s spread. In particular, biomedical imaging could help to visualize the internal organs of the human body and disorders that affect them. One of them is chest X-rays (CXRs) which has widely been used for preventive medicine or disease screening. However, when it comes to detecting COVID-19 from CXR images, most of the approaches rely on standard image classification algorithms, which have limitations with low identification accuracy and improper extraction of key features. As a result, a convolutional neural network (CNN)-based fusion network has been developed for automated COVID-19 screening in this study. First, using attention networks and multiple fine-tuned CNN models, we extract key features that are resistant to overfitting. We then employ a locally connected layer to create a weighted combination of these models for final COVID-19 detection. Using a publicly available dataset of CXR images from healthy subjects as well as COVID-19 and pneumonia cases, we evaluated the predictive capabilities of our proposed model. Test results demonstrate that the proposed fusion model performs favorably compared to individual CNN models.https://ieeexplore.ieee.org/document/10226595/Attention mechanismbiomedical imagingchest X-ray (CXR) imagingCOVID-19deep learningmodel fusion |
| spellingShingle | M. Shamim Hossain Mohammad Shorfuzzaman Noninvasive COVID-19 Screening Using Deep-Learning-Based Multilevel Fusion Model With an Attention Mechanism IEEE Open Journal of Instrumentation and Measurement Attention mechanism biomedical imaging chest X-ray (CXR) imaging COVID-19 deep learning model fusion |
| title | Noninvasive COVID-19 Screening Using Deep-Learning-Based Multilevel Fusion Model With an Attention Mechanism |
| title_full | Noninvasive COVID-19 Screening Using Deep-Learning-Based Multilevel Fusion Model With an Attention Mechanism |
| title_fullStr | Noninvasive COVID-19 Screening Using Deep-Learning-Based Multilevel Fusion Model With an Attention Mechanism |
| title_full_unstemmed | Noninvasive COVID-19 Screening Using Deep-Learning-Based Multilevel Fusion Model With an Attention Mechanism |
| title_short | Noninvasive COVID-19 Screening Using Deep-Learning-Based Multilevel Fusion Model With an Attention Mechanism |
| title_sort | noninvasive covid 19 screening using deep learning based multilevel fusion model with an attention mechanism |
| topic | Attention mechanism biomedical imaging chest X-ray (CXR) imaging COVID-19 deep learning model fusion |
| url | https://ieeexplore.ieee.org/document/10226595/ |
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