Disease Recognition in X-ray Images with Doctor Consultation-Inspired Model
The application of chest X-ray imaging for early disease screening is attracting interest from the computer vision and deep learning community. To date, various deep learning models have been applied in X-ray image analysis. However, models perform inconsistently depending on the dataset. In this pa...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-12-01
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| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/8/12/323 |
| _version_ | 1797456930130624512 |
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| author | Kim Anh Phung Thuan Trong Nguyen Nileshkumar Wangad Samah Baraheem Nguyen D. Vo Khang Nguyen |
| author_facet | Kim Anh Phung Thuan Trong Nguyen Nileshkumar Wangad Samah Baraheem Nguyen D. Vo Khang Nguyen |
| author_sort | Kim Anh Phung |
| collection | DOAJ |
| description | The application of chest X-ray imaging for early disease screening is attracting interest from the computer vision and deep learning community. To date, various deep learning models have been applied in X-ray image analysis. However, models perform inconsistently depending on the dataset. In this paper, we consider each individual model as a medical doctor. We then propose a doctor consultation-inspired method that fuses multiple models. In particular, we consider both early and late fusion mechanisms for consultation. The early fusion mechanism combines the deep learned features from multiple models, whereas the late fusion method combines the confidence scores of all individual models. Experiments on two X-ray imaging datasets demonstrate the superiority of the proposed method relative to baseline. The experimental results also show that early consultation consistently outperforms the late consultation mechanism in both benchmark datasets. In particular, the early doctor consultation-inspired model outperforms all individual models by a large margin, i.e., 3.03 and 1.86 in terms of accuracy in the UIT COVID-19 and chest X-ray datasets, respectively. |
| first_indexed | 2024-03-09T16:14:55Z |
| format | Article |
| id | doaj.art-6ed0830470604f10806ed7ff206f3580 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-09T16:14:55Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-6ed0830470604f10806ed7ff206f35802023-11-24T15:52:11ZengMDPI AGJournal of Imaging2313-433X2022-12-0181232310.3390/jimaging8120323Disease Recognition in X-ray Images with Doctor Consultation-Inspired ModelKim Anh Phung0Thuan Trong Nguyen1Nileshkumar Wangad2Samah Baraheem3Nguyen D. Vo4Khang Nguyen5Department of Computer Science, University of Dayton, Dayton, OH 45469, USAFaculty of Software Engineering, University of Information Technology, Linh Trung Ward, Thu Duc District, Ho Chi Minh City 70000, VietnamDepartment of Computer Science, University of Dayton, Dayton, OH 45469, USADepartment of Computer Science, University of Dayton, Dayton, OH 45469, USAFaculty of Software Engineering, University of Information Technology, Linh Trung Ward, Thu Duc District, Ho Chi Minh City 70000, VietnamFaculty of Software Engineering, University of Information Technology, Linh Trung Ward, Thu Duc District, Ho Chi Minh City 70000, VietnamThe application of chest X-ray imaging for early disease screening is attracting interest from the computer vision and deep learning community. To date, various deep learning models have been applied in X-ray image analysis. However, models perform inconsistently depending on the dataset. In this paper, we consider each individual model as a medical doctor. We then propose a doctor consultation-inspired method that fuses multiple models. In particular, we consider both early and late fusion mechanisms for consultation. The early fusion mechanism combines the deep learned features from multiple models, whereas the late fusion method combines the confidence scores of all individual models. Experiments on two X-ray imaging datasets demonstrate the superiority of the proposed method relative to baseline. The experimental results also show that early consultation consistently outperforms the late consultation mechanism in both benchmark datasets. In particular, the early doctor consultation-inspired model outperforms all individual models by a large margin, i.e., 3.03 and 1.86 in terms of accuracy in the UIT COVID-19 and chest X-ray datasets, respectively.https://www.mdpi.com/2313-433X/8/12/323disease recognitionmedical image processingdoctor consultation-inspired |
| spellingShingle | Kim Anh Phung Thuan Trong Nguyen Nileshkumar Wangad Samah Baraheem Nguyen D. Vo Khang Nguyen Disease Recognition in X-ray Images with Doctor Consultation-Inspired Model Journal of Imaging disease recognition medical image processing doctor consultation-inspired |
| title | Disease Recognition in X-ray Images with Doctor Consultation-Inspired Model |
| title_full | Disease Recognition in X-ray Images with Doctor Consultation-Inspired Model |
| title_fullStr | Disease Recognition in X-ray Images with Doctor Consultation-Inspired Model |
| title_full_unstemmed | Disease Recognition in X-ray Images with Doctor Consultation-Inspired Model |
| title_short | Disease Recognition in X-ray Images with Doctor Consultation-Inspired Model |
| title_sort | disease recognition in x ray images with doctor consultation inspired model |
| topic | disease recognition medical image processing doctor consultation-inspired |
| url | https://www.mdpi.com/2313-433X/8/12/323 |
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