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Ensemble of CheXNet and VGG-19 Feature Extractor with Random Forest Classifier for Pediatric Pneumonia Detection

Pneumonia, an acute respiratory infection, causes serious breathing hindrance by damaging lung/s. Recovery of pneumonia patients depends on the early diagnosis of the disease and proper treatment. This paper proposes an ensemble method-based pneumonia diagnosis from Chest X-ray images. The deep Conv...

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Autores principales: Habib, Nahida, Hasan, Md. Mahmodul, Reza, Md. Mahfuz, Rahman, Mohammad Motiur
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Singapore 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7597433/
https://www.ncbi.nlm.nih.gov/pubmed/33163973
http://dx.doi.org/10.1007/s42979-020-00373-y
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author Habib, Nahida
Hasan, Md. Mahmodul
Reza, Md. Mahfuz
Rahman, Mohammad Motiur
author_facet Habib, Nahida
Hasan, Md. Mahmodul
Reza, Md. Mahfuz
Rahman, Mohammad Motiur
author_sort Habib, Nahida
collection PubMed
description Pneumonia, an acute respiratory infection, causes serious breathing hindrance by damaging lung/s. Recovery of pneumonia patients depends on the early diagnosis of the disease and proper treatment. This paper proposes an ensemble method-based pneumonia diagnosis from Chest X-ray images. The deep Convolutional Neural Networks (CNNs)—CheXNet and VGG-19 are trained and used to extract features from given X-ray images. These features are then ensembled for classification. To overcome data irregularity problem, Random Under Sampler (RUS), Random Over Sampler (ROS) and Synthetic Minority Oversampling Technique (SMOTE) are applied on the ensembled feature vector. The ensembled feature vector is then classified using several Machine Learning (ML) classification techniques (Random Forest, Adaptive Boosting, K-Nearest Neighbors). Among these methods, Random Forest got better performance metrics than others on the available standard dataset. Comparison with existing methods shows that the proposed method attains improved classification accuracy, AUC values and outperforms all other models providing 98.93% accurate prediction. The model also exhibits potential generalization capacity when tested on different dataset. Outcomes of this study can be great to use for pneumonia diagnosis from chest X-ray images.
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spelling pubmed-75974332020-11-02 Ensemble of CheXNet and VGG-19 Feature Extractor with Random Forest Classifier for Pediatric Pneumonia Detection Habib, Nahida Hasan, Md. Mahmodul Reza, Md. Mahfuz Rahman, Mohammad Motiur SN Comput Sci Original Research Pneumonia, an acute respiratory infection, causes serious breathing hindrance by damaging lung/s. Recovery of pneumonia patients depends on the early diagnosis of the disease and proper treatment. This paper proposes an ensemble method-based pneumonia diagnosis from Chest X-ray images. The deep Convolutional Neural Networks (CNNs)—CheXNet and VGG-19 are trained and used to extract features from given X-ray images. These features are then ensembled for classification. To overcome data irregularity problem, Random Under Sampler (RUS), Random Over Sampler (ROS) and Synthetic Minority Oversampling Technique (SMOTE) are applied on the ensembled feature vector. The ensembled feature vector is then classified using several Machine Learning (ML) classification techniques (Random Forest, Adaptive Boosting, K-Nearest Neighbors). Among these methods, Random Forest got better performance metrics than others on the available standard dataset. Comparison with existing methods shows that the proposed method attains improved classification accuracy, AUC values and outperforms all other models providing 98.93% accurate prediction. The model also exhibits potential generalization capacity when tested on different dataset. Outcomes of this study can be great to use for pneumonia diagnosis from chest X-ray images. Springer Singapore 2020-10-30 2020 /pmc/articles/PMC7597433/ /pubmed/33163973 http://dx.doi.org/10.1007/s42979-020-00373-y Text en © Springer Nature Singapore Pte Ltd 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Research
Habib, Nahida
Hasan, Md. Mahmodul
Reza, Md. Mahfuz
Rahman, Mohammad Motiur
Ensemble of CheXNet and VGG-19 Feature Extractor with Random Forest Classifier for Pediatric Pneumonia Detection
title Ensemble of CheXNet and VGG-19 Feature Extractor with Random Forest Classifier for Pediatric Pneumonia Detection
title_full Ensemble of CheXNet and VGG-19 Feature Extractor with Random Forest Classifier for Pediatric Pneumonia Detection
title_fullStr Ensemble of CheXNet and VGG-19 Feature Extractor with Random Forest Classifier for Pediatric Pneumonia Detection
title_full_unstemmed Ensemble of CheXNet and VGG-19 Feature Extractor with Random Forest Classifier for Pediatric Pneumonia Detection
title_short Ensemble of CheXNet and VGG-19 Feature Extractor with Random Forest Classifier for Pediatric Pneumonia Detection
title_sort ensemble of chexnet and vgg-19 feature extractor with random forest classifier for pediatric pneumonia detection
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7597433/
https://www.ncbi.nlm.nih.gov/pubmed/33163973
http://dx.doi.org/10.1007/s42979-020-00373-y
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