An Approach for Thoracic Syndrome Classification with Convolutional Neural Networks

There have been remarkable changes in our lives and the way we perceive the world with advances in computing technology. Healthcare sector is evolving with the intervention of the latest computer-driven technology and has made a remarkable change in the diagnosis and treatment of various diseases. D...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Sapna Juneja, Abhinav Juneja, Gaurav Dhiman, Sanchit Behl, Sandeep Kautish
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/227306bcd11e47128a50e24ad4a20df7
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:227306bcd11e47128a50e24ad4a20df7
record_format	dspace
spelling	oai:doaj.org-article:227306bcd11e47128a50e24ad4a20df72021-11-08T02:36:48ZAn Approach for Thoracic Syndrome Classification with Convolutional Neural Networks1748-671810.1155/2021/3900254https://doaj.org/article/227306bcd11e47128a50e24ad4a20df72021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/3900254https://doaj.org/toc/1748-6718There have been remarkable changes in our lives and the way we perceive the world with advances in computing technology. Healthcare sector is evolving with the intervention of the latest computer-driven technology and has made a remarkable change in the diagnosis and treatment of various diseases. Due to many governing factors including air pollution, there is a rapid rise in chest-related diseases and the number of such patients is rising at an alarming rate. In this research work, we have employed machine learning approach for the detecting various chest-related problems using convolutional neural networks (CNN) on an open dataset of chest X-rays. The method has an edge over the traditional approaches for image segmentation including thresholding, k-means clustering, and edge detection. The CNN cannot scan and process the whole image at an instant; it needs to recursively scan small pixel spots until it has scanned the whole image. Spatial transformation layers and VGG19 have been used for the purpose of feature extraction, and ReLU activation function has been employed due to its inherent low complexity and high computation efficiency; finally, stochastic gradient descent has been used as an optimizer. The main advantage of the current method is that it retains the essential features of the image for prediction along with incorporating a considerable dimensional reduction. The model delivered substantial improvement over existing research in terms of precision, f-score, and accuracy of prediction. This model if used precisely can be very effective for healthcare practitioners in determining the thoracic or pneumonic symptoms in the patient at an early stage thus guiding the practitioner to start the treatment immediately leading to fast improvement in the health status of the patient.Sapna JunejaAbhinav JunejaGaurav DhimanSanchit BehlSandeep KautishHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7ENComputational and Mathematical Methods in Medicine, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Sapna Juneja Abhinav Juneja Gaurav Dhiman Sanchit Behl Sandeep Kautish An Approach for Thoracic Syndrome Classification with Convolutional Neural Networks
description	There have been remarkable changes in our lives and the way we perceive the world with advances in computing technology. Healthcare sector is evolving with the intervention of the latest computer-driven technology and has made a remarkable change in the diagnosis and treatment of various diseases. Due to many governing factors including air pollution, there is a rapid rise in chest-related diseases and the number of such patients is rising at an alarming rate. In this research work, we have employed machine learning approach for the detecting various chest-related problems using convolutional neural networks (CNN) on an open dataset of chest X-rays. The method has an edge over the traditional approaches for image segmentation including thresholding, k-means clustering, and edge detection. The CNN cannot scan and process the whole image at an instant; it needs to recursively scan small pixel spots until it has scanned the whole image. Spatial transformation layers and VGG19 have been used for the purpose of feature extraction, and ReLU activation function has been employed due to its inherent low complexity and high computation efficiency; finally, stochastic gradient descent has been used as an optimizer. The main advantage of the current method is that it retains the essential features of the image for prediction along with incorporating a considerable dimensional reduction. The model delivered substantial improvement over existing research in terms of precision, f-score, and accuracy of prediction. This model if used precisely can be very effective for healthcare practitioners in determining the thoracic or pneumonic symptoms in the patient at an early stage thus guiding the practitioner to start the treatment immediately leading to fast improvement in the health status of the patient.
format	article
author	Sapna Juneja Abhinav Juneja Gaurav Dhiman Sanchit Behl Sandeep Kautish
author_facet	Sapna Juneja Abhinav Juneja Gaurav Dhiman Sanchit Behl Sandeep Kautish
author_sort	Sapna Juneja
title	An Approach for Thoracic Syndrome Classification with Convolutional Neural Networks
title_short	An Approach for Thoracic Syndrome Classification with Convolutional Neural Networks
title_full	An Approach for Thoracic Syndrome Classification with Convolutional Neural Networks
title_fullStr	An Approach for Thoracic Syndrome Classification with Convolutional Neural Networks
title_full_unstemmed	An Approach for Thoracic Syndrome Classification with Convolutional Neural Networks
title_sort	approach for thoracic syndrome classification with convolutional neural networks
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/227306bcd11e47128a50e24ad4a20df7
work_keys_str_mv	AT sapnajuneja anapproachforthoracicsyndromeclassificationwithconvolutionalneuralnetworks AT abhinavjuneja anapproachforthoracicsyndromeclassificationwithconvolutionalneuralnetworks AT gauravdhiman anapproachforthoracicsyndromeclassificationwithconvolutionalneuralnetworks AT sanchitbehl anapproachforthoracicsyndromeclassificationwithconvolutionalneuralnetworks AT sandeepkautish anapproachforthoracicsyndromeclassificationwithconvolutionalneuralnetworks AT sapnajuneja approachforthoracicsyndromeclassificationwithconvolutionalneuralnetworks AT abhinavjuneja approachforthoracicsyndromeclassificationwithconvolutionalneuralnetworks AT gauravdhiman approachforthoracicsyndromeclassificationwithconvolutionalneuralnetworks AT sanchitbehl approachforthoracicsyndromeclassificationwithconvolutionalneuralnetworks AT sandeepkautish approachforthoracicsyndromeclassificationwithconvolutionalneuralnetworks
_version_	1718443073735753728

An Approach for Thoracic Syndrome Classification with Convolutional Neural Networks

Ejemplares similares