Cutting-edge multi-task model: unveiling COVID-19 through fusion of image processing algorithms
The COVID-19 pandemic underscores the vital need for accurate lung infection diagnosis to guide effective medical interventions. In response, this research introduces a novel deep multi-task model that seamlessly integrates segmentation and classification tasks for the detection of COVID-19 in CT sc...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21681163.2023.2287521 |
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| _version_ | 1846149857391149056 |
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| author | Shirin Kordnoori Maliheh Sabeti Hamidreza Mostafaei Saeed Seyed Agha Banihashemi |
| author_facet | Shirin Kordnoori Maliheh Sabeti Hamidreza Mostafaei Saeed Seyed Agha Banihashemi |
| author_sort | Shirin Kordnoori |
| collection | DOAJ |
| description | The COVID-19 pandemic underscores the vital need for accurate lung infection diagnosis to guide effective medical interventions. In response, this research introduces a novel deep multi-task model that seamlessly integrates segmentation and classification tasks for the detection of COVID-19 in CT scan images. This innovative model leverages a shared encoder for feature extraction, a dedicated decoder for segmentation, and a multi-layer perceptron for classification. The primary objective of this model is to address the challenge of task imbalance introduced by the application of image processing algorithms in the multi-task models. Our study involves a two-stage evaluation. Initially, we apply the proposed multi-task model with image processing algorithms to highlight task imbalance. Subsequently, we balance tasks by combining binary image processing algorithms. Evaluation on four datasets shows impressive results with a Dice coefficient of 88.91 ± 0.01 for segmentation and 0.97 classification accuracy. In summary, this model advances medical image analysis for enhanced diagnostic precision in healthcare. |
| format | Article |
| id | doaj-art-e90ed3cfb6b04a7da618d6f824df657d |
| institution | Kabale University |
| issn | 2168-1163 2168-1171 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization |
| spelling | doaj-art-e90ed3cfb6b04a7da618d6f824df657d2024-11-29T10:29:56ZengTaylor & Francis GroupComputer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization2168-11632168-11712024-12-0112110.1080/21681163.2023.2287521Cutting-edge multi-task model: unveiling COVID-19 through fusion of image processing algorithmsShirin Kordnoori0Maliheh Sabeti1Hamidreza Mostafaei2Saeed Seyed Agha Banihashemi3Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, IranDepartment of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, IranDepartment of Statistics, North Tehran Branch, Islamic Azad University, Tehran, IranDepartment of Mathematics, North Tehran Branch, Islamic Azad University, Tehran, IranThe COVID-19 pandemic underscores the vital need for accurate lung infection diagnosis to guide effective medical interventions. In response, this research introduces a novel deep multi-task model that seamlessly integrates segmentation and classification tasks for the detection of COVID-19 in CT scan images. This innovative model leverages a shared encoder for feature extraction, a dedicated decoder for segmentation, and a multi-layer perceptron for classification. The primary objective of this model is to address the challenge of task imbalance introduced by the application of image processing algorithms in the multi-task models. Our study involves a two-stage evaluation. Initially, we apply the proposed multi-task model with image processing algorithms to highlight task imbalance. Subsequently, we balance tasks by combining binary image processing algorithms. Evaluation on four datasets shows impressive results with a Dice coefficient of 88.91 ± 0.01 for segmentation and 0.97 classification accuracy. In summary, this model advances medical image analysis for enhanced diagnostic precision in healthcare.https://www.tandfonline.com/doi/10.1080/21681163.2023.2287521Medical image analysisimage enhancementmulti-task learningCovid-19 diagnosis |
| spellingShingle | Shirin Kordnoori Maliheh Sabeti Hamidreza Mostafaei Saeed Seyed Agha Banihashemi Cutting-edge multi-task model: unveiling COVID-19 through fusion of image processing algorithms Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization Medical image analysis image enhancement multi-task learning Covid-19 diagnosis |
| title | Cutting-edge multi-task model: unveiling COVID-19 through fusion of image processing algorithms |
| title_full | Cutting-edge multi-task model: unveiling COVID-19 through fusion of image processing algorithms |
| title_fullStr | Cutting-edge multi-task model: unveiling COVID-19 through fusion of image processing algorithms |
| title_full_unstemmed | Cutting-edge multi-task model: unveiling COVID-19 through fusion of image processing algorithms |
| title_short | Cutting-edge multi-task model: unveiling COVID-19 through fusion of image processing algorithms |
| title_sort | cutting edge multi task model unveiling covid 19 through fusion of image processing algorithms |
| topic | Medical image analysis image enhancement multi-task learning Covid-19 diagnosis |
| url | https://www.tandfonline.com/doi/10.1080/21681163.2023.2287521 |
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