Analysis of lung scan imaging using deep multi‐task learning structure for Covid‐19 disease
Abstract Covid‐19 caused by the SARS‐CoV2 virus has become a pandemic all over the world. By growing in a number of cases, there is a need for clinical decision‐making system based on machine learning models. Most of the previous studies have examined only one task, while the detection and identific...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-04-01
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| Series: | IET Image Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ipr2.12736 |
| _version_ | 1797852204585975808 |
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| author | Shirin Kordnoori Malihe Sabeti Hamidreza Mostafaei Saeed Seyed Agha Banihashemi |
| author_facet | Shirin Kordnoori Malihe Sabeti Hamidreza Mostafaei Saeed Seyed Agha Banihashemi |
| author_sort | Shirin Kordnoori |
| collection | DOAJ |
| description | Abstract Covid‐19 caused by the SARS‐CoV2 virus has become a pandemic all over the world. By growing in a number of cases, there is a need for clinical decision‐making system based on machine learning models. Most of the previous studies have examined only one task, while the detection and identification of infectious area are conducted simultaneously in the real world. Thus, the present study aims to propose a multi‐task model which can perform automatic classification‐segmentation for screening Covid‐19 pneumonia by using chest CT imaging. This model includes a common encoder for feature representation, one decoder for segmentation, and a multi‐layer perceptron for classification, respectively. The proposed model can evaluate three datasets, along with the effect of images size on the output of the model. The outputs were examined in both multi‐task and single‐task learning. The result indicates that the effect of multi‐task is significant in improving the results, which can increase the outputs of each task performance to 95.40% accuracy in classification and 95.40% in segmentation. Further, the model represented the highest results among the state‐of‐the‐art methods. The proposed model can be applied as a primary screening tool to help primary service staff in better referral of the suspected patients to specialists. |
| first_indexed | 2024-04-09T19:30:05Z |
| format | Article |
| id | doaj.art-852aab26bff640869dda38a4f6d6f389 |
| institution | Directory Open Access Journal |
| issn | 1751-9659 1751-9667 |
| language | English |
| last_indexed | 2024-04-09T19:30:05Z |
| publishDate | 2023-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj.art-852aab26bff640869dda38a4f6d6f3892023-04-05T04:26:02ZengWileyIET Image Processing1751-96591751-96672023-04-011751534154510.1049/ipr2.12736Analysis of lung scan imaging using deep multi‐task learning structure for Covid‐19 diseaseShirin Kordnoori0Malihe 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 IranAbstract Covid‐19 caused by the SARS‐CoV2 virus has become a pandemic all over the world. By growing in a number of cases, there is a need for clinical decision‐making system based on machine learning models. Most of the previous studies have examined only one task, while the detection and identification of infectious area are conducted simultaneously in the real world. Thus, the present study aims to propose a multi‐task model which can perform automatic classification‐segmentation for screening Covid‐19 pneumonia by using chest CT imaging. This model includes a common encoder for feature representation, one decoder for segmentation, and a multi‐layer perceptron for classification, respectively. The proposed model can evaluate three datasets, along with the effect of images size on the output of the model. The outputs were examined in both multi‐task and single‐task learning. The result indicates that the effect of multi‐task is significant in improving the results, which can increase the outputs of each task performance to 95.40% accuracy in classification and 95.40% in segmentation. Further, the model represented the highest results among the state‐of‐the‐art methods. The proposed model can be applied as a primary screening tool to help primary service staff in better referral of the suspected patients to specialists.https://doi.org/10.1049/ipr2.12736image classificationimage segmentation |
| spellingShingle | Shirin Kordnoori Malihe Sabeti Hamidreza Mostafaei Saeed Seyed Agha Banihashemi Analysis of lung scan imaging using deep multi‐task learning structure for Covid‐19 disease IET Image Processing image classification image segmentation |
| title | Analysis of lung scan imaging using deep multi‐task learning structure for Covid‐19 disease |
| title_full | Analysis of lung scan imaging using deep multi‐task learning structure for Covid‐19 disease |
| title_fullStr | Analysis of lung scan imaging using deep multi‐task learning structure for Covid‐19 disease |
| title_full_unstemmed | Analysis of lung scan imaging using deep multi‐task learning structure for Covid‐19 disease |
| title_short | Analysis of lung scan imaging using deep multi‐task learning structure for Covid‐19 disease |
| title_sort | analysis of lung scan imaging using deep multi task learning structure for covid 19 disease |
| topic | image classification image segmentation |
| url | https://doi.org/10.1049/ipr2.12736 |
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