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

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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書誌詳細
主要な著者:	Haruna, Chiroma, Abdul kareem, Sameem, Umar, Ibrahim, Ahmad, Gadam, Abdulmumini , Garba, Abubakar, Adamu, Fatihu, Mukhtar, Herawan, Tutut
フォーマット:	論文
言語:	English English
出版事項:	Czech Technical University in Prague, Faculty of Transportation Sciences 2015
主題:	Z665 Library Science. Information Science
オンライン･アクセス:	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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Malaria severity classification through Jordan-Elman neural network based on features extracted from thick blood smear

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