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Detection of the Malpositioned Catheters and Endotracheal Tubes on Radiographs using Deep Learning Methods

Rungta, Ankit (2021) Detection of the Malpositioned Catheters and Endotracheal Tubes on Radiographs using Deep Learning Methods. Masters thesis, Dublin, National College of Ireland.

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Deep learning advancements have resulted in wonderful outcomes for a range of recent image processing research studies in medicine. Chest X-Rays are the most often performed radiological examination and are an particularly important modality that is being researched extensively for a variety of purposes. One of them is tube and line placement, which is usually verified by a radiologist because of the significant problems that might occur from incorrect placement. Delays are to be expected when radiologists are engaged with other scans, which offers an opportunity for human error. As COVID-19 accelerates, it will become increasingly critical to detect misplaced catheters and lines more quickly and accurately as more individuals are intubated and linked to a ventilator. Deep learning systems can help prioritize radiographs to interpret potentially misplaced catheters and lines. As a result, knowledge of contemporary algorithms was gained, as well as the major problems associated with creating a viable deep learning model for identifying catheter and tube locations on radiographs. This paper proposes a unique technique for classifying normal and malpositioned tubes on chest radiographs using EfficientNet CNN along with CBAM (Convolutional Block Attention Module) as a novel approach and EfficientNet CNN as a baseline approach. This investigation will assist in developing the machine learning techniques for this critical application.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
R Medicine > R Medicine (General)
H Social Sciences > HM Sociology > Information Science > Communication > Medical Informatics
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Clara Chan
Date Deposited: 14 Dec 2021 12:57
Last Modified: 14 Dec 2021 12:57

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