An Improved Transfer Learning-Based Model for Malaria Detection using Blood Smear of Microscopic Cell Images

Basit Öğe Kaydı
dc.authoridVarol, Asaf/0000-0003-1606-4079en_US
dc.authoridMuhammad, Bilyaminu/0000-0003-4281-5729en_US
dc.contributor.authorBilyaminu, Muhammad
dc.contributor.authorVarol, Asaf
dc.date.accessioned2024-07-12T21:40:38Z
dc.date.available2024-07-12T21:40:38Z
dc.date.issued2021en_US
dc.department[Belirlenecek]en_US
dc.description2nd International Informatics and Software Engineering Conference (IISEC) - Artificial Intelligence for Digital Transformation -- DEC 16-17, 2021 -- Ankara, TURKEYen_US
dc.description.abstractBecause of insufficient medical specialists in some parts of the African and Asian continents, malaria patients' mortality rates have increased over the years. Since the people of regions generally suffer from malaria diseases, computer-aided detection (CAD) technology is required to decrease the number of casualties and reduce the waiting time for consulting by a Malaria specialist. This study shows the potential of transfer learning, a method of Deep Learning (DL) to classify the smeared blood of microscopic malaria cell images to determine whether it is parasitized or uninfected. This classification of malaria cell images will enhance the workflow of health practitioners at the frontline, especially microscopists, and provides them with a valuable alternative for malaria detection based on microscopic cell images. Although many technological advancements and evaluation techniques for identifying the infection exist, a microscopist at regions with limited resources faces challenges in improving diagnostic accuracy. We compared and evaluated a type of pre-trained CNN models, such as ResNet-50 and our appended Resnet-50+KNN. The experiment shows that our new model has the excellent capability and can perform better on malarial microscopic cell image classification with a higher accuracy rate of 98%.en_US
dc.description.sponsorshipIEEE Turkey Secten_US
dc.identifier.doi10.1109/IISEC54230.2021.9672447
dc.identifier.isbn978-1-6654-0759-5
dc.identifier.scopus2-s2.0-85125336957en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/IISEC54230.2021.9672447
dc.identifier.urihttps://hdl.handle.net/20.500.12415/7399
dc.identifier.wosWOS:000841548300050en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2nd International Informatics And Software Engineering Conference (Iisec)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKY08740
dc.subjectMalariaen_US
dc.subjectDeep Learningen_US
dc.subjectTransfer Learningen_US
dc.subjectResnet-50en_US
dc.subjectMicroscopic Cell Imagesen_US
dc.titleAn Improved Transfer Learning-Based Model for Malaria Detection using Blood Smear of Microscopic Cell Imagesen_US
dc.typeConference Object
dspace.entity.typePublication

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu