A HYBRID APPROACH FOR MALARIA CLASSIFICATION USING CNN-BASED FEATURE EXTRACTION AND TRADITIONAL MACHINE LEARNING CLASSIFIERS

Malaria is a major global health threat, and timely and correct diagnosis is essential for effective treatment. Traditional diagnostic methods, such as the microscopic examination of blood smears, are time-consuming and require expert personnel. The study presents a mix of machine learning methods...

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Bibliographic Details
Main Authors:	omar Mohammed Alzakholi, Walat A. Ahmed, Bafreen N. Mohammed, Asaad Kh. Ibrahim
Format:	Article
Language:	English
Published:	University of Zakho 2025-07-01
Series:	Science Journal of University of Zakho
Subjects:	Malaria classification Convolutional Neural Networks (CNNs) Web vulnerabilities, feature extraction, feature selection, data mining, supervised machine learning, phishing websites, cyber security, and phishing identification Malaria classification, Convolutional Neural Networks (CNN), Feature extraction, Random Forest, Logistic Regression, KNN, SVM, Gradient Boosting. Heart Disease Prediction, Logistic Regression, Support Vector Machine, Feature Selection, Linear Discriminant Analysis, Cleveland data. KNN estimator
Online Access:	http://sjuoz.uoz.edu.krd/index.php/sjuoz/article/view/1489
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A HYBRID APPROACH FOR MALARIA CLASSIFICATION USING CNN-BASED FEATURE EXTRACTION AND TRADITIONAL MACHINE LEARNING CLASSIFIERS

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