Chest X-Rays Abnormalities Localization and Classification Using an Ensemble Framework of Deep Convolutional Neural Networks
Medical X-rays are one of the primary choices for diagnosis because of their potential to disclose previously undetected pathologic changes, non-invasive qualities, radiation dosage, and cost concerns. There are several advantages to creating computer-aided detection (CAD) technologies for X-Ray ana...
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| Format: | Article |
| Language: | English |
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World Scientific Publishing
2023-02-01
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| Series: | Vietnam Journal of Computer Science |
| Subjects: | |
| Online Access: | https://www.worldscientific.com/doi/10.1142/S2196888822500348 |
| _version_ | 1797894870795288576 |
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| author | Vu-Thu-Nguyet Pham Quang-Chung Nguyen Quang-Vu Nguyen |
| author_facet | Vu-Thu-Nguyet Pham Quang-Chung Nguyen Quang-Vu Nguyen |
| author_sort | Vu-Thu-Nguyet Pham |
| collection | DOAJ |
| description | Medical X-rays are one of the primary choices for diagnosis because of their potential to disclose previously undetected pathologic changes, non-invasive qualities, radiation dosage, and cost concerns. There are several advantages to creating computer-aided detection (CAD) technologies for X-Ray analysis. With the advancement of technology, researchers have lately used the deep learning approach to obtain high accuracy outcomes in the CAD system. With CAD, computer output may be utilized as a backup option for radiologists, assisting doctors in making the best selections. Chest X-Rays (CXRs) are commonly used to diagnose heart and lung problems. Automatically recognizing these problems with high accuracy might considerably improve real-world diagnosis processes. However, the lack of standard publicly available datasets and benchmark research makes comparing and establishing the best detection algorithms challenging. In order to overcome these difficulties, we have used the VinDr-CXR dataset, which is one of the latest public datasets including 18,000 expert-annotated images labeled into 22 local position-specific abnormalities and 6 globally suspected diseases. To improve the identification of chest abnormalities, we proposed a data preparation procedure and a novel model based on YOLOv5 and ResNet50. YOLOv5 is the most recent YOLO series, and it is more adaptable than previous one-stage detection algorithms. In our paper, the role of YOLOv5 is to locate the abnormality location. On the other side, we employ ResNet for classification, avoiding gradient explosion concerns in deep learning. Then we filter the YOLOv5 and ResNet results. The YOLOv5 detection result is updated if ResNet determines that the image is not anomalous. |
| first_indexed | 2024-04-10T07:17:20Z |
| format | Article |
| id | doaj.art-6d35c5c3cf91498ba0528cbcb7bc1f1f |
| institution | Directory Open Access Journal |
| issn | 2196-8888 2196-8896 |
| language | English |
| last_indexed | 2024-04-10T07:17:20Z |
| publishDate | 2023-02-01 |
| publisher | World Scientific Publishing |
| record_format | Article |
| series | Vietnam Journal of Computer Science |
| spelling | doaj.art-6d35c5c3cf91498ba0528cbcb7bc1f1f2023-02-25T04:51:01ZengWorld Scientific PublishingVietnam Journal of Computer Science2196-88882196-88962023-02-011001557310.1142/S2196888822500348Chest X-Rays Abnormalities Localization and Classification Using an Ensemble Framework of Deep Convolutional Neural NetworksVu-Thu-Nguyet Pham0Quang-Chung Nguyen1Quang-Vu Nguyen2The University of Danang, Vietnam – Korea University of Information and Communication Technology, 470 Tran Dai Nghia Street, Ngu Hanh Son District, Danang 550000, VietnamThe University of Danang, Vietnam – Korea University of Information and Communication Technology, 470 Tran Dai Nghia Street, Ngu Hanh Son District, Danang 550000, VietnamThe University of Danang, Vietnam – Korea University of Information and Communication Technology, 470 Tran Dai Nghia Street, Ngu Hanh Son District, Danang 550000, VietnamMedical X-rays are one of the primary choices for diagnosis because of their potential to disclose previously undetected pathologic changes, non-invasive qualities, radiation dosage, and cost concerns. There are several advantages to creating computer-aided detection (CAD) technologies for X-Ray analysis. With the advancement of technology, researchers have lately used the deep learning approach to obtain high accuracy outcomes in the CAD system. With CAD, computer output may be utilized as a backup option for radiologists, assisting doctors in making the best selections. Chest X-Rays (CXRs) are commonly used to diagnose heart and lung problems. Automatically recognizing these problems with high accuracy might considerably improve real-world diagnosis processes. However, the lack of standard publicly available datasets and benchmark research makes comparing and establishing the best detection algorithms challenging. In order to overcome these difficulties, we have used the VinDr-CXR dataset, which is one of the latest public datasets including 18,000 expert-annotated images labeled into 22 local position-specific abnormalities and 6 globally suspected diseases. To improve the identification of chest abnormalities, we proposed a data preparation procedure and a novel model based on YOLOv5 and ResNet50. YOLOv5 is the most recent YOLO series, and it is more adaptable than previous one-stage detection algorithms. In our paper, the role of YOLOv5 is to locate the abnormality location. On the other side, we employ ResNet for classification, avoiding gradient explosion concerns in deep learning. Then we filter the YOLOv5 and ResNet results. The YOLOv5 detection result is updated if ResNet determines that the image is not anomalous.https://www.worldscientific.com/doi/10.1142/S2196888822500348Chest X-raysabnormalities detectiondeep learningYOLOv5ResNet50medical informatics |
| spellingShingle | Vu-Thu-Nguyet Pham Quang-Chung Nguyen Quang-Vu Nguyen Chest X-Rays Abnormalities Localization and Classification Using an Ensemble Framework of Deep Convolutional Neural Networks Vietnam Journal of Computer Science Chest X-rays abnormalities detection deep learning YOLOv5 ResNet50 medical informatics |
| title | Chest X-Rays Abnormalities Localization and Classification Using an Ensemble Framework of Deep Convolutional Neural Networks |
| title_full | Chest X-Rays Abnormalities Localization and Classification Using an Ensemble Framework of Deep Convolutional Neural Networks |
| title_fullStr | Chest X-Rays Abnormalities Localization and Classification Using an Ensemble Framework of Deep Convolutional Neural Networks |
| title_full_unstemmed | Chest X-Rays Abnormalities Localization and Classification Using an Ensemble Framework of Deep Convolutional Neural Networks |
| title_short | Chest X-Rays Abnormalities Localization and Classification Using an Ensemble Framework of Deep Convolutional Neural Networks |
| title_sort | chest x rays abnormalities localization and classification using an ensemble framework of deep convolutional neural networks |
| topic | Chest X-rays abnormalities detection deep learning YOLOv5 ResNet50 medical informatics |
| url | https://www.worldscientific.com/doi/10.1142/S2196888822500348 |
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