Examining the potential of deep learning in the early diagnosis of Alzheimer's disease using brain MRI images
Yükleniyor...
Dosyalar
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Millions of people around the world are affected by Alzheimer Diseases, which is a major
public health issue worldwide. The key to effective treatment and management techniques
is early diagnosis of the illness. For the purpose of detecting Alzheimer Diseases using MRI
data, we looked into and evaluated three distinct DL models in this study. The first model
used is CNN with two convolutional and two fully connected layers served as the initial
model. The second model was an improved version of the first, with a leaky ReLU activation
function, more fully connected layers, and a larger kernel size. The third model was a transfer
learning model with two dense layers that was built on top of the VGG16 architecture. An
extensive set of MRI scans from Alzheimer's patients and healthy controls was used to train
the models. The first and second CNN models achieved an accuracy of 96%, while the
transfer learning model achieved an accuracy of 81%, according to the accuracy, precision,
recall, and F1-score measurements. In conclusion, MRI data-based Alzheimer's diagnosis
may benefit from DL models. However, further progress is required to improve these models'
performance and accessibility for clinical use.
Açıklama
Anahtar Kelimeler
AD, Brain Imaging, CNN, Transfer Learning, VGG-16
Kaynak
WoS Q Değeri
Scopus Q Değeri
Cilt
Sayı
Künye
Mahmood, A. S. M. (2023). Examining the potential of deep learning in the early diagnosis of Alzheimer's disease using brain MRI images. (Yayınlanmamış yüksek lisans tezi). Altınbaş Üniversitesi, Lisansüstü Eğitim Enstitüsü, İstanbul.
