Pneumonia detection in chest X-ray images using compound scaled deep learning model

Pneumonia is the leading cause of death worldwide for children under 5 years of age. For pneumonia diagnosis, chest X-rays are examined by trained radiologists. However, this process is tedious and time-consuming. Biomedical image diagnosis techniques show great potential in medical image examinatio...

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Autores principales:	Mohammad Farukh Hashmi, Satyarth Katiyar, Abdul Wahab Hashmi, Avinash G. Keskar
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	pneumonia chest x-ray images convolution neural network (cnn) resnet50 transfer learning Control engineering systems. Automatic machinery (General) TJ212-225 Automation T59.5
Acceso en línea:	https://doaj.org/article/849ff0d85c314db08ae3517cf884b6ce
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id	oai:doaj.org-article:849ff0d85c314db08ae3517cf884b6ce
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spelling	oai:doaj.org-article:849ff0d85c314db08ae3517cf884b6ce2021-11-04T15:00:41ZPneumonia detection in chest X-ray images using compound scaled deep learning model0005-11441848-338010.1080/00051144.2021.1973297https://doaj.org/article/849ff0d85c314db08ae3517cf884b6ce2021-10-01T00:00:00Zhttp://dx.doi.org/10.1080/00051144.2021.1973297https://doaj.org/toc/0005-1144https://doaj.org/toc/1848-3380Pneumonia is the leading cause of death worldwide for children under 5 years of age. For pneumonia diagnosis, chest X-rays are examined by trained radiologists. However, this process is tedious and time-consuming. Biomedical image diagnosis techniques show great potential in medical image examination. A model for the identification of pneumonia, trained on chest X-ray images, has been proposed in this paper. The compound scaled ResNet50, which is the upscaled version of ResNet50, has been used in this paper. ResNet50 is a multilayer layer convolution neural network having residual blocks. As it was very difficult to obtain a sufficiently large dataset for detection tasks, data augmentation techniques were used to increase the training dataset. Transfer learning is also used while training the models. The proposed model could help in detecting the disease and can assist the radiologists in their clinical decision-making process. The model was evaluated and statistically validated to overfitting and generalization errors. Different scores, such as testing accuracy, F1, recall, precision and AUC score, were computed to check the efficacy of the proposed model. The proposed model attained a test accuracy of 98.14% and an AUC score of 99.71 on the test data from the Guangzhou Women and Children’s Medical Center pneumonia dataset.Mohammad Farukh HashmiSatyarth KatiyarAbdul Wahab HashmiAvinash G. KeskarTaylor & Francis Grouparticlepneumoniachest x-ray imagesconvolution neural network (cnn)resnet50transfer learningControl engineering systems. Automatic machinery (General)TJ212-225AutomationT59.5ENAutomatika, Vol 62, Iss 3-4, Pp 397-406 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	pneumonia chest x-ray images convolution neural network (cnn) resnet50 transfer learning Control engineering systems. Automatic machinery (General) TJ212-225 Automation T59.5
spellingShingle	pneumonia chest x-ray images convolution neural network (cnn) resnet50 transfer learning Control engineering systems. Automatic machinery (General) TJ212-225 Automation T59.5 Mohammad Farukh Hashmi Satyarth Katiyar Abdul Wahab Hashmi Avinash G. Keskar Pneumonia detection in chest X-ray images using compound scaled deep learning model
description	Pneumonia is the leading cause of death worldwide for children under 5 years of age. For pneumonia diagnosis, chest X-rays are examined by trained radiologists. However, this process is tedious and time-consuming. Biomedical image diagnosis techniques show great potential in medical image examination. A model for the identification of pneumonia, trained on chest X-ray images, has been proposed in this paper. The compound scaled ResNet50, which is the upscaled version of ResNet50, has been used in this paper. ResNet50 is a multilayer layer convolution neural network having residual blocks. As it was very difficult to obtain a sufficiently large dataset for detection tasks, data augmentation techniques were used to increase the training dataset. Transfer learning is also used while training the models. The proposed model could help in detecting the disease and can assist the radiologists in their clinical decision-making process. The model was evaluated and statistically validated to overfitting and generalization errors. Different scores, such as testing accuracy, F1, recall, precision and AUC score, were computed to check the efficacy of the proposed model. The proposed model attained a test accuracy of 98.14% and an AUC score of 99.71 on the test data from the Guangzhou Women and Children’s Medical Center pneumonia dataset.
format	article
author	Mohammad Farukh Hashmi Satyarth Katiyar Abdul Wahab Hashmi Avinash G. Keskar
author_facet	Mohammad Farukh Hashmi Satyarth Katiyar Abdul Wahab Hashmi Avinash G. Keskar
author_sort	Mohammad Farukh Hashmi
title	Pneumonia detection in chest X-ray images using compound scaled deep learning model
title_short	Pneumonia detection in chest X-ray images using compound scaled deep learning model
title_full	Pneumonia detection in chest X-ray images using compound scaled deep learning model
title_fullStr	Pneumonia detection in chest X-ray images using compound scaled deep learning model
title_full_unstemmed	Pneumonia detection in chest X-ray images using compound scaled deep learning model
title_sort	pneumonia detection in chest x-ray images using compound scaled deep learning model
publisher	Taylor & Francis Group
publishDate	2021
url	https://doaj.org/article/849ff0d85c314db08ae3517cf884b6ce
work_keys_str_mv	AT mohammadfarukhhashmi pneumoniadetectioninchestxrayimagesusingcompoundscaleddeeplearningmodel AT satyarthkatiyar pneumoniadetectioninchestxrayimagesusingcompoundscaleddeeplearningmodel AT abdulwahabhashmi pneumoniadetectioninchestxrayimagesusingcompoundscaleddeeplearningmodel AT avinashgkeskar pneumoniadetectioninchestxrayimagesusingcompoundscaleddeeplearningmodel
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Pneumonia detection in chest X-ray images using compound scaled deep learning model

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