Malaria parasite detection using deep learning algorithms
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Dosyalar
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The most astounding achievements in domains as disparate as robotics, NLP, image
recognition, AI, and so on are the result of deep learning, an automated learning technique.
one of the main benefits of deep learning is that the model only needs to be trained and taught
once, in contrast to machine learning. Training and teaching in machine learning must be
redone whenever the input changes. Red blood cell (RBC) examination under a microscope
is the standard method for detecting malaria, which affects millions of people worldwide.
Manual microscopic malaria diagnosis is difficult due to time restrictions and a lack of
expertise. Using a data-driven approach to education and training, we are able to assess
whether or not a person in a photograph is suffering from malaria. This proposed system
aids medical students, physicians, and others in diagnosing malaria. we are working on a
way for Giemsa-stained smears to detect parasites in the blood, such as those that cause
malaria. When the sample size is small, deep learning algorithms perform poorly. Transfer
learning means getting visual features from large general datasets and using limited problemspecific datasets to solve classification problems that are specific to that problem. in this
study, we use transfer learning to find and sort malaria parasites. we employ three commonly
used pre-trained CNN models: VGG19, ResNet50, and MobileNetV2. the NIH Malaria
Dataset is used in all experimental evaluations, and the algorithm is found to be 100%
accurate in detecting malaria.
Açıklama
Anahtar Kelimeler
Artificial Intelligence, Deep Learning, CNN, Malaria Diagnosis, Malaria Parasite
Kaynak
WoS Q Değeri
Scopus Q Değeri
Cilt
Sayı
Künye
Alnussairi, M. H. D. (2023). Malaria parasite detection using deep learning algorithms. (Yayınlanmamış doktora tezi). Altınbaş Üniversitesi, Lisansüstü Eğitim Enstitüsü, İstanbul.
