Deep Learning-Based Object Detection Strategies for Disease Detection and Localization in Chest X-Ray Images
Background and Objectives: Chest X-ray (CXR) images are commonly used to diagnose respiratory and cardiovascular diseases. However, traditional manual interpretation is often subjective, time-consuming, and prone to errors, leading to inconsistent detection accuracy and poor generalization. In this...
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| Format: | Article |
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MDPI AG
2024-11-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/14/23/2636 |
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| author | Yi-Ching Cheng Yi-Chieh Hung Guan-Hua Huang Tai-Been Chen Nan-Han Lu Kuo-Ying Liu Kuo-Hsuan Lin |
| author_facet | Yi-Ching Cheng Yi-Chieh Hung Guan-Hua Huang Tai-Been Chen Nan-Han Lu Kuo-Ying Liu Kuo-Hsuan Lin |
| author_sort | Yi-Ching Cheng |
| collection | DOAJ |
| description | Background and Objectives: Chest X-ray (CXR) images are commonly used to diagnose respiratory and cardiovascular diseases. However, traditional manual interpretation is often subjective, time-consuming, and prone to errors, leading to inconsistent detection accuracy and poor generalization. In this paper, we present deep learning-based object detection methods for automatically identifying and annotating abnormal regions in CXR images. Methods: We developed and tested our models using disease-labeled CXR images and location-bounding boxes from E-Da Hospital. Given the prevalence of normal images over diseased ones in clinical settings, we created various training datasets and approaches to assess how different proportions of background images impact model performance. To address the issue of limited examples for certain diseases, we also investigated few-shot object detection techniques. We compared convolutional neural networks (CNNs) and Transformer-based models to determine the most effective architecture for medical image analysis. Results: The findings show that background image proportions greatly influenced model inference. Moreover, schemes incorporating binary classification consistently improved performance, and CNN-based models outperformed Transformer-based models across all scenarios. Conclusions: We have developed a more efficient and reliable system for the automated detection of disease labels and location bounding boxes in CXR images. |
| format | Article |
| id | doaj-art-201bc6a7384b4bb29e3d67ed28f60784 |
| institution | DOAJ |
| issn | 2075-4418 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-201bc6a7384b4bb29e3d67ed28f607842025-08-20T02:50:18ZengMDPI AGDiagnostics2075-44182024-11-011423263610.3390/diagnostics14232636Deep Learning-Based Object Detection Strategies for Disease Detection and Localization in Chest X-Ray ImagesYi-Ching Cheng0Yi-Chieh Hung1Guan-Hua Huang2Tai-Been Chen3Nan-Han Lu4Kuo-Ying Liu5Kuo-Hsuan Lin6Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu 300093, TaiwanInstitute of Statistics, National Yang Ming Chiao Tung University, Hsinchu 300093, TaiwanInstitute of Statistics, National Yang Ming Chiao Tung University, Hsinchu 300093, TaiwanDepartment of Radiological Technology, Faculty of Medical Technology, Teikyo University, Tokyo 173-8605, JapanDepartment of Radiology, E-Da Cancer Hospital, I-Shou University, Kaohsiung 824005, TaiwanDepartment of Radiology, E-Da Cancer Hospital, I-Shou University, Kaohsiung 824005, TaiwanDepartment of Emergency Medicine, E-Da Hospital, I-Shou University, Kaohsiung 824005, TaiwanBackground and Objectives: Chest X-ray (CXR) images are commonly used to diagnose respiratory and cardiovascular diseases. However, traditional manual interpretation is often subjective, time-consuming, and prone to errors, leading to inconsistent detection accuracy and poor generalization. In this paper, we present deep learning-based object detection methods for automatically identifying and annotating abnormal regions in CXR images. Methods: We developed and tested our models using disease-labeled CXR images and location-bounding boxes from E-Da Hospital. Given the prevalence of normal images over diseased ones in clinical settings, we created various training datasets and approaches to assess how different proportions of background images impact model performance. To address the issue of limited examples for certain diseases, we also investigated few-shot object detection techniques. We compared convolutional neural networks (CNNs) and Transformer-based models to determine the most effective architecture for medical image analysis. Results: The findings show that background image proportions greatly influenced model inference. Moreover, schemes incorporating binary classification consistently improved performance, and CNN-based models outperformed Transformer-based models across all scenarios. Conclusions: We have developed a more efficient and reliable system for the automated detection of disease labels and location bounding boxes in CXR images.https://www.mdpi.com/2075-4418/14/23/2636chest X-raysdeep learningfew-shot object detectionobject detection |
| spellingShingle | Yi-Ching Cheng Yi-Chieh Hung Guan-Hua Huang Tai-Been Chen Nan-Han Lu Kuo-Ying Liu Kuo-Hsuan Lin Deep Learning-Based Object Detection Strategies for Disease Detection and Localization in Chest X-Ray Images Diagnostics chest X-rays deep learning few-shot object detection object detection |
| title | Deep Learning-Based Object Detection Strategies for Disease Detection and Localization in Chest X-Ray Images |
| title_full | Deep Learning-Based Object Detection Strategies for Disease Detection and Localization in Chest X-Ray Images |
| title_fullStr | Deep Learning-Based Object Detection Strategies for Disease Detection and Localization in Chest X-Ray Images |
| title_full_unstemmed | Deep Learning-Based Object Detection Strategies for Disease Detection and Localization in Chest X-Ray Images |
| title_short | Deep Learning-Based Object Detection Strategies for Disease Detection and Localization in Chest X-Ray Images |
| title_sort | deep learning based object detection strategies for disease detection and localization in chest x ray images |
| topic | chest X-rays deep learning few-shot object detection object detection |
| url | https://www.mdpi.com/2075-4418/14/23/2636 |
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