Generic Foreign Object Detection in Chest X-rays

Santosh, K. C.; Roy, Shotabdi; Allu, Siva

doi:10.1007/978-3-031-07005-1_10

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…Its exceptional performance has made it one of the leading real-time object detection methods. Additionally, it has applications in fields such as healthcare, national defense, and security [8][9][10]. It uses the scalable and efficient layer aggregation network E-ELAN to accelerate model convergence.…”

Section: Introductionmentioning

confidence: 99%

YOLO-CID: Improved YOLOv7 for X-ray Contraband Image Detection

Gan,

Wan,

Lei

et al. 2023

Electronics

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Currently, X-ray inspection systems may produce false detections due to factors such as the varying sizes of contraband images, complex backgrounds, and blurred edges. To address this issue, we propose the YOLO-CID method for contraband image detection. Firstly, we designed the MP-OD module in the backbone network to enhance the model’s ability to extract key information from complex background images. Secondly, at the neck of the network, we designed a simplified version of BiFPN to add cross-scale connection lines in the feature fusion structure, to preserve deeper semantic information and enhance the network’s ability to represent objects in low-contrast or occlusion situations. Finally, we added a new object detection layer to improve the model’s accuracy in detecting small objects in dense environments. Experimental results on the PIDray public dataset show that the average accuracy rate of the YOLO-CID algorithm is 82.7% and the recall rate is 81.2%, which are 4.9% and 3.2% higher than the YOLOv7 algorithm, respectively. At the same time, the mAP on the CLCXray dataset reached 80.2%. Additionally, it can achieve a real-time detection speed of 40 frames per second and 43 frames per second in real scenes. These results demonstrate the effectiveness of the YOLO-CID algorithm in X-ray contraband detection.

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Section: Introductionmentioning

confidence: 99%

YOLO-CID: Improved YOLOv7 for X-ray Contraband Image Detection

Gan,

Wan,

Lei

et al. 2023

Electronics

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Analyzing Overlaid Foreign Objects in Chest X-rays—Clinical Significance and Artificial Intelligence Tools

Roy

Santosh

2023

Healthcare

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The presence of non-biomedical foreign objects (NBFO), such as coins, buttons and jewelry, and biomedical foreign objects (BFO), such as medical tubes and devices in chest X-rays (CXRs), make accurate interpretation difficult, as they do not indicate known biological abnormalities like excess fluids, tuberculosis (TB) or cysts. Such foreign objects need to be detected, localized, categorized as either NBFO or BFO, and removed from CXR or highlighted in CXR for effective abnormality analysis. Very specifically, NBFOs can adversely impact the process, as typical machine learning algorithms would consider these objects to be biological abnormalities producing false-positive cases. It holds true for BFOs in CXRs. This paper examines detailed discussions on numerous clinical reports in addition to computer-aided detection (CADe) with diagnosis (CADx) tools, where both shallow learning and deep learning algorithms are applied. Our discussion reflects the importance of accurately detecting, isolating, classifying, and either removing or highlighting NBFOs and BFOs in CXRs by taking 29 peer-reviewed research reports and articles into account.

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Generic Foreign Object Detection in Chest X-rays

Cited by 2 publications

References 27 publications

YOLO-CID: Improved YOLOv7 for X-ray Contraband Image Detection

YOLO-CID: Improved YOLOv7 for X-ray Contraband Image Detection

Analyzing Overlaid Foreign Objects in Chest X-rays—Clinical Significance and Artificial Intelligence Tools

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