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Malaria severity classification through Jordan-Elman neural network based on features extracted from thick blood smear

This article presents an alternative approach useful for medical prac- titioners who wish to detect malaria and accurately identify the level of severity. Malaria classifiers are usually based on feed forward neural networks. In this study, the proposed classifier is developed based on the Jordan...

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Main Authors: Haruna, Chiroma, Abdul kareem, Sameem, Umar, Ibrahim, Ahmad, Gadam, Abdulmumini , Garba, Abubakar, Adamu, Fatihu, Mukhtar, Herawan, Tutut
Format: Article
Language:English
English
Published: Czech Technical University in Prague, Faculty of Transportation Sciences 2015
Subjects:
Z665 Library Science. Information Science
Online Access:http://irep.iium.edu.my/46647/1/NNW.2015.25.028.pdf
http://irep.iium.edu.my/46647/4/46647_Malaria_severity_classification_through_Jordan-Elman_neural_network_WOS.pdf
http://irep.iium.edu.my/46647/
http://khis.khu.ac.kr:9090/SummonRecord/FETCH-LOGICAL-p521-5050a2458441821306734c03fd8ca088b3a9662fe6f3e363cee8675633ac83f43
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spelling my.iium.irep.466472017-11-16T09:42:09Z http://irep.iium.edu.my/46647/ Malaria severity classification through Jordan-Elman neural network based on features extracted from thick blood smear Haruna, Chiroma Abdul kareem, Sameem Umar, Ibrahim Ahmad, Gadam Abdulmumini , Garba Abubakar, Adamu Fatihu, Mukhtar Herawan, Tutut Z665 Library Science. Information Science This article presents an alternative approach useful for medical prac- titioners who wish to detect malaria and accurately identify the level of severity. Malaria classifiers are usually based on feed forward neural networks. In this study, the proposed classifier is developed based on the Jordan-Elman neural networks. Its performance is evaluated using a receiver-operating characteristic curve, sensitiv- ity, specificity, positive predictive value, negative predictive value, confusion matrix, mean square error, determinant coefficient, and reliability. The effectiveness of the classifier is compared to a support vector machine and multiple regression models. The results of the comparative analysis demonstrate a superior performance level of the Jordan-Elman neural network model. Further comparison of the classier with previous literature indicates performance improvement over existing results. The Jordan-Elman neural networks classifier can assist medical practitioners in the fast detection of malaria and determining its severity, especially in tropical and subtropical regions where cases of malaria are prevalent Czech Technical University in Prague, Faculty of Transportation Sciences 2015-09 Article REM application/pdf en http://irep.iium.edu.my/46647/1/NNW.2015.25.028.pdf application/pdf en http://irep.iium.edu.my/46647/4/46647_Malaria_severity_classification_through_Jordan-Elman_neural_network_WOS.pdf Haruna, Chiroma and Abdul kareem, Sameem and Umar, Ibrahim and Ahmad, Gadam and Abdulmumini , Garba and Abubakar, Adamu and Fatihu, Mukhtar and Herawan, Tutut (2015) Malaria severity classification through Jordan-Elman neural network based on features extracted from thick blood smear. Neural Network World , 5 (15). pp. 565-584. ISSN 1210-0552 http://khis.khu.ac.kr:9090/SummonRecord/FETCH-LOGICAL-p521-5050a2458441821306734c03fd8ca088b3a9662fe6f3e363cee8675633ac83f43 10.14311/NNW.2015.25.028
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic Z665 Library Science. Information Science
spellingShingle Z665 Library Science. Information Science
Haruna, Chiroma
Abdul kareem, Sameem
Umar, Ibrahim
Ahmad, Gadam
Abdulmumini , Garba
Abubakar, Adamu
Fatihu, Mukhtar
Herawan, Tutut
Malaria severity classification through Jordan-Elman neural network based on features extracted from thick blood smear
description This article presents an alternative approach useful for medical prac- titioners who wish to detect malaria and accurately identify the level of severity. Malaria classifiers are usually based on feed forward neural networks. In this study, the proposed classifier is developed based on the Jordan-Elman neural networks. Its performance is evaluated using a receiver-operating characteristic curve, sensitiv- ity, specificity, positive predictive value, negative predictive value, confusion matrix, mean square error, determinant coefficient, and reliability. The effectiveness of the classifier is compared to a support vector machine and multiple regression models. The results of the comparative analysis demonstrate a superior performance level of the Jordan-Elman neural network model. Further comparison of the classier with previous literature indicates performance improvement over existing results. The Jordan-Elman neural networks classifier can assist medical practitioners in the fast detection of malaria and determining its severity, especially in tropical and subtropical regions where cases of malaria are prevalent
format Article
author Haruna, Chiroma
Abdul kareem, Sameem
Umar, Ibrahim
Ahmad, Gadam
Abdulmumini , Garba
Abubakar, Adamu
Fatihu, Mukhtar
Herawan, Tutut
author_facet Haruna, Chiroma
Abdul kareem, Sameem
Umar, Ibrahim
Ahmad, Gadam
Abdulmumini , Garba
Abubakar, Adamu
Fatihu, Mukhtar
Herawan, Tutut
author_sort Haruna, Chiroma
title Malaria severity classification through Jordan-Elman neural network based on features extracted from thick blood smear
title_short Malaria severity classification through Jordan-Elman neural network based on features extracted from thick blood smear
title_full Malaria severity classification through Jordan-Elman neural network based on features extracted from thick blood smear
title_fullStr Malaria severity classification through Jordan-Elman neural network based on features extracted from thick blood smear
title_full_unstemmed Malaria severity classification through Jordan-Elman neural network based on features extracted from thick blood smear
title_sort malaria severity classification through jordan-elman neural network based on features extracted from thick blood smear
publisher Czech Technical University in Prague, Faculty of Transportation Sciences
publishDate 2015
url http://irep.iium.edu.my/46647/1/NNW.2015.25.028.pdf
http://irep.iium.edu.my/46647/4/46647_Malaria_severity_classification_through_Jordan-Elman_neural_network_WOS.pdf
http://irep.iium.edu.my/46647/
http://khis.khu.ac.kr:9090/SummonRecord/FETCH-LOGICAL-p521-5050a2458441821306734c03fd8ca088b3a9662fe6f3e363cee8675633ac83f43
_version_ 1643613037718929408
score 13.211869

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