2023
DOI: 10.3390/jcm12185841
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Chest X-ray Foreign Objects Detection Using Artificial Intelligence

Jakub Kufel,
Katarzyna Bargieł-Łączek,
Maciej Koźlik
et al.

Abstract: Diagnostic imaging has become an integral part of the healthcare system. In recent years, scientists around the world have been working on artificial intelligence-based tools that help in achieving better and faster diagnoses. Their accuracy is crucial for successful treatment, especially for imaging diagnostics. This study used a deep convolutional neural network to detect four categories of objects on digital chest X-ray images. The data were obtained from the publicly available National Institutes of Health… Show more

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Cited by 5 publications
(2 citation statements)
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“…In addition to the success of the detection, we also analyze the execution time on lightweight devices. As a benchmark, we employ YOLOv8 [24], which is a state-of-the-art neural network that is utilized in recent research to detect foreign objects in various applications and environments [3,14,29,34,[41][42][43]65]. Depending on the model size and resources, YOLOv8 can be executed on embedded devices with low latency [31].…”
Section: Discussionmentioning
confidence: 99%
“…In addition to the success of the detection, we also analyze the execution time on lightweight devices. As a benchmark, we employ YOLOv8 [24], which is a state-of-the-art neural network that is utilized in recent research to detect foreign objects in various applications and environments [3,14,29,34,[41][42][43]65]. Depending on the model size and resources, YOLOv8 can be executed on embedded devices with low latency [31].…”
Section: Discussionmentioning
confidence: 99%
“…Despite the known drawbacks of the YOLO model, the YOLOv8 model has been used in various medical image applications in the field of radiology. In studies involving radiography and MRI, these models have demonstrated high accuracy in detecting conditions such as osteochondritis dissecans in elbow radiographs, identifying foreign objects in chest radiographs, and detecting tumors in brain MRI scans [38][39][40].…”
Section: Discussionmentioning
confidence: 99%