A Novel Method for Multivariant Pneumonia Classification Based on Hybrid CNN-PCA Based Feature Extraction Using Extreme Learning Machine With CXR Images

In this era of COVID19, proper diagnosis and treatment of pneumonia are very important. Chest X-Ray (CXR) image analysis plays a vital role in the reliable diagnosis of pneumonia. An experienced radiologist is required for this. However, even for an experienced radiographer, it is quite challenging...

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Autores principales:	Md. Nahiduzzaman, Md. Omaer Faruq Goni, Md. Shamim Anower, Md. Robiul Islam, Mominul Ahsan, Julfikar Haider, Saravanakumar Gurusamy, Rakibul Hassan, Md. Rakibul Islam
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Chest X-Ray (CXR) convolutional neural network (CNN) contrast limited adaptive histogram equalization (CLAHE) extreme learning machine (ELM) feature extraction principal component analysis (PCA) Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/f1b94c2133aa40a2b4ae5619a828f88f
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spelling	oai:doaj.org-article:f1b94c2133aa40a2b4ae5619a828f88f2021-11-18T00:11:01ZA Novel Method for Multivariant Pneumonia Classification Based on Hybrid CNN-PCA Based Feature Extraction Using Extreme Learning Machine With CXR Images2169-353610.1109/ACCESS.2021.3123782https://doaj.org/article/f1b94c2133aa40a2b4ae5619a828f88f2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9591581/https://doaj.org/toc/2169-3536In this era of COVID19, proper diagnosis and treatment of pneumonia are very important. Chest X-Ray (CXR) image analysis plays a vital role in the reliable diagnosis of pneumonia. An experienced radiologist is required for this. However, even for an experienced radiographer, it is quite challenging and time-consuming to diagnose accurately due to the fuzziness of CXR images. Also, identification can be erroneous due to the involvement of human judgement. Hence, an authentic and automated system can play an important role here. In this era of cutting-edge technology, deep learning (DL) is highly used in every sector. There are several existing methods to diagnose pneumonia but they have accuracy problems. In this study, an automatic pneumonia detection system has been proposed by applying the extreme learning machine (ELM) on the Kaggle CXR images (Pneumonia). Three models have been studied: classification using extreme learning machine (ELM), ELM with a hybrid convolutional neural network-principal component analysis (CNN-PCA) based feature extraction, and CNN-PCA-ELM with the CXR images which are contrast-enhanced by contrast limited adaptive histogram equalization (CLAHE). Among these three proposed methods, the final model provides an optimistic result. It achieves the recall score of 98% and accuracy score of 98.32% for multiclass pneumonia classification. On the other hand, a binary classification achieves 100% recall and 99.83% accuracy. The proposed method also outperforms the existing methods. The outcome has been compared using several benchmarks that include accuracy, precision, recall, etc.Md. NahiduzzamanMd. Omaer Faruq GoniMd. Shamim AnowerMd. Robiul IslamMominul AhsanJulfikar HaiderSaravanakumar GurusamyRakibul HassanMd. Rakibul IslamIEEEarticleChest X-Ray (CXR)convolutional neural network (CNN)contrast limited adaptive histogram equalization (CLAHE)extreme learning machine (ELM)feature extractionprincipal component analysis (PCA)Electrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 147512-147526 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Chest X-Ray (CXR) convolutional neural network (CNN) contrast limited adaptive histogram equalization (CLAHE) extreme learning machine (ELM) feature extraction principal component analysis (PCA) Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Chest X-Ray (CXR) convolutional neural network (CNN) contrast limited adaptive histogram equalization (CLAHE) extreme learning machine (ELM) feature extraction principal component analysis (PCA) Electrical engineering. Electronics. Nuclear engineering TK1-9971 Md. Nahiduzzaman Md. Omaer Faruq Goni Md. Shamim Anower Md. Robiul Islam Mominul Ahsan Julfikar Haider Saravanakumar Gurusamy Rakibul Hassan Md. Rakibul Islam A Novel Method for Multivariant Pneumonia Classification Based on Hybrid CNN-PCA Based Feature Extraction Using Extreme Learning Machine With CXR Images
description	In this era of COVID19, proper diagnosis and treatment of pneumonia are very important. Chest X-Ray (CXR) image analysis plays a vital role in the reliable diagnosis of pneumonia. An experienced radiologist is required for this. However, even for an experienced radiographer, it is quite challenging and time-consuming to diagnose accurately due to the fuzziness of CXR images. Also, identification can be erroneous due to the involvement of human judgement. Hence, an authentic and automated system can play an important role here. In this era of cutting-edge technology, deep learning (DL) is highly used in every sector. There are several existing methods to diagnose pneumonia but they have accuracy problems. In this study, an automatic pneumonia detection system has been proposed by applying the extreme learning machine (ELM) on the Kaggle CXR images (Pneumonia). Three models have been studied: classification using extreme learning machine (ELM), ELM with a hybrid convolutional neural network-principal component analysis (CNN-PCA) based feature extraction, and CNN-PCA-ELM with the CXR images which are contrast-enhanced by contrast limited adaptive histogram equalization (CLAHE). Among these three proposed methods, the final model provides an optimistic result. It achieves the recall score of 98% and accuracy score of 98.32% for multiclass pneumonia classification. On the other hand, a binary classification achieves 100% recall and 99.83% accuracy. The proposed method also outperforms the existing methods. The outcome has been compared using several benchmarks that include accuracy, precision, recall, etc.
format	article
author	Md. Nahiduzzaman Md. Omaer Faruq Goni Md. Shamim Anower Md. Robiul Islam Mominul Ahsan Julfikar Haider Saravanakumar Gurusamy Rakibul Hassan Md. Rakibul Islam
author_facet	Md. Nahiduzzaman Md. Omaer Faruq Goni Md. Shamim Anower Md. Robiul Islam Mominul Ahsan Julfikar Haider Saravanakumar Gurusamy Rakibul Hassan Md. Rakibul Islam
author_sort	Md. Nahiduzzaman
title	A Novel Method for Multivariant Pneumonia Classification Based on Hybrid CNN-PCA Based Feature Extraction Using Extreme Learning Machine With CXR Images
title_short	A Novel Method for Multivariant Pneumonia Classification Based on Hybrid CNN-PCA Based Feature Extraction Using Extreme Learning Machine With CXR Images
title_full	A Novel Method for Multivariant Pneumonia Classification Based on Hybrid CNN-PCA Based Feature Extraction Using Extreme Learning Machine With CXR Images
title_fullStr	A Novel Method for Multivariant Pneumonia Classification Based on Hybrid CNN-PCA Based Feature Extraction Using Extreme Learning Machine With CXR Images
title_full_unstemmed	A Novel Method for Multivariant Pneumonia Classification Based on Hybrid CNN-PCA Based Feature Extraction Using Extreme Learning Machine With CXR Images
title_sort	novel method for multivariant pneumonia classification based on hybrid cnn-pca based feature extraction using extreme learning machine with cxr images
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/f1b94c2133aa40a2b4ae5619a828f88f
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A Novel Method for Multivariant Pneumonia Classification Based on Hybrid CNN-PCA Based Feature Extraction Using Extreme Learning Machine With CXR Images

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