Detection of the location of pneumothorax in chest X-rays using small artificial neural networks and a simple training process

Abstract The purpose of this study was to evaluate the diagnostic performance achieved by using fully-connected small artificial neural networks (ANNs) and a simple training process, the Kim-Monte Carlo algorithm, to detect the location of pneumothorax in chest X-rays. A total of 1,000 chest X-ray i...

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Autores principales:	Yongil Cho, Jong Soo Kim, Tae Ho Lim, Inhye Lee, Jongbong Choi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/47512f2075b54dc1bb0fc62757da5de9
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spelling	oai:doaj.org-article:47512f2075b54dc1bb0fc62757da5de92021-12-02T16:06:10ZDetection of the location of pneumothorax in chest X-rays using small artificial neural networks and a simple training process10.1038/s41598-021-92523-22045-2322https://doaj.org/article/47512f2075b54dc1bb0fc62757da5de92021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92523-2https://doaj.org/toc/2045-2322Abstract The purpose of this study was to evaluate the diagnostic performance achieved by using fully-connected small artificial neural networks (ANNs) and a simple training process, the Kim-Monte Carlo algorithm, to detect the location of pneumothorax in chest X-rays. A total of 1,000 chest X-ray images with pneumothorax were taken randomly from NIH (the National Institutes of Health) public image database and used as the training and test sets. Each X-ray image with pneumothorax was divided into 49 boxes for pneumothorax localization. For each of the boxes in the chest X-ray images contained in the test set, the area under the receiver operating characteristic (ROC) curve (AUC) was 0.882, and the sensitivity and specificity were 80.6% and 83.0%, respectively. In addition, a common currently used deep-learning method for image recognition, the convolution neural network (CNN), was also applied to the same dataset for comparison purposes. The performance of the fully-connected small ANN was better than that of the CNN. Regarding the diagnostic performances of the CNN with different activation functions, the CNN with a sigmoid activation function for fully-connected hidden nodes was better than the CNN with the rectified linear unit (RELU) activation function. This study showed that our approach can accurately detect the location of pneumothorax in chest X-rays, significantly reduce the time delay incurred when diagnosing urgent diseases such as pneumothorax, and increase the effectiveness of clinical practice and patient care.Yongil ChoJong Soo KimTae Ho LimInhye LeeJongbong ChoiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yongil Cho Jong Soo Kim Tae Ho Lim Inhye Lee Jongbong Choi Detection of the location of pneumothorax in chest X-rays using small artificial neural networks and a simple training process
description	Abstract The purpose of this study was to evaluate the diagnostic performance achieved by using fully-connected small artificial neural networks (ANNs) and a simple training process, the Kim-Monte Carlo algorithm, to detect the location of pneumothorax in chest X-rays. A total of 1,000 chest X-ray images with pneumothorax were taken randomly from NIH (the National Institutes of Health) public image database and used as the training and test sets. Each X-ray image with pneumothorax was divided into 49 boxes for pneumothorax localization. For each of the boxes in the chest X-ray images contained in the test set, the area under the receiver operating characteristic (ROC) curve (AUC) was 0.882, and the sensitivity and specificity were 80.6% and 83.0%, respectively. In addition, a common currently used deep-learning method for image recognition, the convolution neural network (CNN), was also applied to the same dataset for comparison purposes. The performance of the fully-connected small ANN was better than that of the CNN. Regarding the diagnostic performances of the CNN with different activation functions, the CNN with a sigmoid activation function for fully-connected hidden nodes was better than the CNN with the rectified linear unit (RELU) activation function. This study showed that our approach can accurately detect the location of pneumothorax in chest X-rays, significantly reduce the time delay incurred when diagnosing urgent diseases such as pneumothorax, and increase the effectiveness of clinical practice and patient care.
format	article
author	Yongil Cho Jong Soo Kim Tae Ho Lim Inhye Lee Jongbong Choi
author_facet	Yongil Cho Jong Soo Kim Tae Ho Lim Inhye Lee Jongbong Choi
author_sort	Yongil Cho
title	Detection of the location of pneumothorax in chest X-rays using small artificial neural networks and a simple training process
title_short	Detection of the location of pneumothorax in chest X-rays using small artificial neural networks and a simple training process
title_full	Detection of the location of pneumothorax in chest X-rays using small artificial neural networks and a simple training process
title_fullStr	Detection of the location of pneumothorax in chest X-rays using small artificial neural networks and a simple training process
title_full_unstemmed	Detection of the location of pneumothorax in chest X-rays using small artificial neural networks and a simple training process
title_sort	detection of the location of pneumothorax in chest x-rays using small artificial neural networks and a simple training process
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/47512f2075b54dc1bb0fc62757da5de9
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Detection of the location of pneumothorax in chest X-rays using small artificial neural networks and a simple training process

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