Malaria parasite detection using deep learning algorithms based on (CNNs) technique

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dc.contributor.authorAlnussairi, Muqdad Hanoon Dawood
dc.contributor.authorİbrahim, Abdullahi Abdu
dc.date.accessioned2022-09-16T07:54:37Z
dc.date.available2022-09-16T07:54:37Z
dc.date.issued2022en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractMalaria is a life-threatening disease caused by female anopheles mosquito bites that are prevalent in many regions of the world. We introduce a deep convolutional neural network (CNN) to improve malaria diagnosis accuracy using patches segmented from microscopic images of red blood cell smears. We design the automatic parasite detection in blood from Giemsa-stained smears using three CNN pre-trained models such as VGG19, ResNet50, and MobileNetV2. As the CNNs are poorly performing for small datasets, we introduce the transfer learning technique. Transfer learning involves acquiring visual features from large general datasets and resolving issues using small datasets. We use a transfer learning approach to detect and classify malaria parasites with three CNN pre-trained models. We evaluated proposed CNN models experimentally using the National Institute of Health (NIH) Malaria Dataset. Our proposed model achieves an accuracy of almost 100%.en_US
dc.identifier.citationAlnussairi, M. H. D., İbrahim, A. A. (2022). Malaria parasite detection using deep learning algorithms based on (CNNs) technique. Computers and Electrical Engineering, 103. 10.1016/j.compeleceng.2022.108316en_US
dc.identifier.issn0045-7906
dc.identifier.scopus2-s2.0-85137015350
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/20.500.12939/2961
dc.identifier.volume103en_US
dc.identifier.wosWOS:000864680600006
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAlnussairi, Muqdad Hanoon Dawood
dc.institutionauthorİbrahim, Abdullahi Abdu
dc.language.isoen
dc.publisherElsevier Ltden_US
dc.relation.ispartofComputers and Electrical Engineering
dc.relation.isversionof10.1016/j.compeleceng.2022.108316en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConvolutional Neural Networken_US
dc.subjectDeep Learningen_US
dc.subjectDisease Detectionen_US
dc.subjectMalaria Dataseten_US
dc.subjectParasite Detectionen_US
dc.subjectPre-Trained Modelsen_US
dc.titleMalaria parasite detection using deep learning algorithms based on (CNNs) technique
dc.typeArticle

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