Malaria parasite detection using deep learning algorithms based on (CNNs) technique
[ X ]
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Malaria 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%.
Açıklama
Anahtar Kelimeler
Convolutional Neural Network, Deep Learning, Disease Detection, Malaria Dataset, Parasite Detection, Pre-Trained Models
Kaynak
Computers and Electrical Engineering
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
103
Sayı
Künye
Alnussairi, 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.2022.108316
