EARLY DETECTION OF MALPOSITIONED CATHETERS AND LINES ON CHEST X-RAYS USING DEEP LEARNING
ABSTARCT :
Hospital patients can have catheters and lines inserted during the course of their admission and serious complications can arise if they are positioned incorrectly. Early recognition of malpositioned tubes is the key to preventing risky complications (even death), even more so now that millions of COVID-19 patients are in the need of these tubes and lines. Earlier detection of malpositioned catheters and lines is even more important as COVID-19 cases continue to surge, these steps can be time consuming and are still prone to human error, especially in stressful situations when hospitals are at capacity. This paper presents an analysis of the solution to the challenge "RANZCR CLiP - Catheter and Line Position Challenge" on Kaggle hosted by Royal Australian and NZ College of Radiologists which scores 0.972 (AUC). It is a Transfer Learning-based CNN heavily inspired by UNet and Efficient Net. This architecture stands out from the rest due to the compound scaling methods applied to achieve its smaller size and higher speed than the rest.
EXISTING SYSTEM :
? Our knowledge exist regarding assessing the position of multiple types of catheters on a single radiograph.
? When encountering difficulties in one of these steps, going back to the previous step can help adherence to this structure.
? In the transverse view, the probe can be tilted and slid upwards or downwards to continue the trajectory and follow the needle tip.
DISADVANTAGE :
? It is also a relatively simple procedure that can easily and rapidly be taught to both inexperienced and experienced physicians.
? However, because catheters are generally not localized and can span the whole radiographic image, bounding boxes are not as useful as they are in other general computer vision problems.
PROPOSED SYSTEM :
• In this work, we propose an automatic approach for detection of catheters and tubes on pediatric X-ray images.
• We propose a simple way of synthesizing catheters on X-ray images to generate a training dataset by exploiting the fact that catheters are essentially tubular structures with various cross sectional profiles.
ADVANTAGE :
? In, ultrasound examination is superior to chest X-ray with regard to time efficiency and comes without harmful radiation.
? The proposed algorithms may demonstrate acceptable performance on the dataset the respective authors curated, but it is difficult to conclude that the algorithm will show sufficient performance at other institutions without further application to other large-scale datasets with a wide variety of cases and catheter profiles.
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