Application of Deep Learning Techniques for Detection of Pneumothorax in Chest Radiographs

doi:10.3390/s23177369

機構典藏 > College of Precision Healthcare > Graduate Institute of Senior Healthcare Management > Journal Article > Item 987654321/124667

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/124667

Title:	Application of Deep Learning Techniques for Detection of Pneumothorax in Chest Radiographs
Authors:	Lawrence Y. Deng, Xiang-Yann Lim , Tang-Yun Luo, Ming-Hsun Lee, Tzu-Ching Lin
Keywords:	artificial intelligence;machine learning;X-ray;magnetic resonance imaging;Detectron2;lung diseases classification;image recognition
Date:	2023-08-24
Issue Date:	2023-10-20 12:05:12 (UTC+8)
Abstract:	With the advent of Artificial Intelligence (AI) and even more so recently in the field of Machine Learning (ML), there has been rapid progress across the field. One of the prominent examples is image recognition in the medical category, such as X-ray imaging, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). It has the potential to alleviate a doctor’s heavy workload of sifting through large quantities of images. Due to the rising attention to lung-related diseases, such as pneumothorax and nodules, ML is being incorporated into the field in the hope of alleviating the already strained medical resources. In this study, we proposed a system that can detect pneumothorax diseases reliably. By comparing multiple models and hyperparameter configurations, we recommend a model for hospitals, as its focus on minimizing false positives aligns with the precision required by medical professionals. Through our cooperation with Poh-Ai Hospital, we acquired a total of over 8000 X-ray images, with more than 1000 of them from pneumothorax patients. We hope that by integrating AI systems into the automated process of scanning chest X-ray images with various diseases, more resources will be available in the already strained medical systems. Our proposed system showed that the best model that is used for transfer learning from our dataset performed with an AP of 51.57 and an AP75 of 61.40, with accuracy at 93.89%, a false positive of 1.12%, and a false negative of 4.99%. Based on the feedback from practicing doctors, they are more wary of false positives. For their use case, we recommend another model due to the lower false positive rate and higher accuracy compared with other models, which in our test shows a rate of only 0.88% and 95.68%, demonstrating the feasibility of the research. This promising result showed that it could be utilized in other types of diseases and expand to more hospitals and medical organizations, potentially benefitting more people.
Relation:	Sensors 2023 23(17),　7369
DOI:	10.3390/s23177369
Appears in Collections:	[Graduate Institute of Senior Healthcare Management] Journal Article

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