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 Chest X-Rays Abnormalities Localization and Classification using An Ensemble Framework of Deep Convolutional Neural Networks
Tác giả hoặc Nhóm tác giả: Vu-Thu-Nguyet Pham, Quang-Chung Nguyen, Quang-Vu Nguyen
Nơi đăng: Vietnam Journal of Computer Science; Số: DOI: 10.1142/S2196888822500348;Từ->đến trang: 1-19;Năm: 2022
Lĩnh vực: Công nghệ thông tin; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
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-worlddiagnosis 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 di±culties, 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-speci¯c abnormalities and 6 globally suspected diseases. To improve the identi¯cation 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 classi¯cation, avoiding gradient explosion concerns in deep learning. Then we ¯lter the YOLOv5 and ResNet results. The YOLOv5 detection result is updated if ResNet determines that the image is not anomalous.
ABSTRACT
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-worlddiagnosis 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 di±culties, 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-speci¯c abnormalities and 6 globally suspected diseases. To improve the identi¯cation 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 classi¯cation, avoiding gradient explosion concerns in deep learning. Then we ¯lter the YOLOv5 and ResNet results. The YOLOv5 detection result is updated if ResNet determines that the image is not anomalous.
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