Author(s):
1. Moneeb Abbas:
National University of Sciences and Technology Islamabad,Pakistan
2. Anum Abdul Salam:
National University of Sciences and Technology Islamabad,Pakistan
3. Jahan Zeb:
National University of Sciences and Technology Islamabad,Pakistan
Abstract:
In recent years, the rapid growth of data in healthcare has prompted a lot of interest in artificial intelligence (AI). Powerful AI algorithms are essential for extracting information from medical data and assisting clinicians in establishing quick and accurate diagnoses of a variety of ailments. In the current COVID-19 outbreak, critically ill patients were intubated and various medical tubes, including an endotracheal tube (ETT), were implanted to protect the airways. The Nasogastric tube (NGT) is used for feeding, whereas the Central Venous Catheter (CVC) is utilized for a variety of medical operations. The adoption of medical protocols by doctors to ensure proper tube installation is a major issue. Manual examination of CXR pictures takes time and frequently leads to misinterpretation. This research aims to create an Automated Medical Tube Detection System that can detect misplaced tubes from chest x-rays (CXR) using deep learning. As a result, using chest xrays to detect poorly positioned tubes can save lives. On CXR the proposed CNN-based EfficientNet architecture efficiently detects and classifies incorrectly positioned tubes. After detailed experimentation, we were able to achieve 0.95 average area under the ROC curve (AUC).
Page(s):
1-1
DOI:
DOI not available
Published:
Journal: IEEE International Conference on Digital Futures and Transformative Technologies (ICoDT2) May 24-26, 2022 (Book of Abstracts), Volume: 1, Issue: 1, Year: 2022
Keywords:
Automatic Detection
,
chest Xrays
,
deep learning with Efficient Net
References:
References are not available for this document.
Citations
Citations are not available for this document.