Çevik, MesutMahmood, Anmar Shukur Mahmood2023-12-082023-12-0820232023Mahmood, 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.https://hdl.handle.net/20.500.12939/4262Millions 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.eninfo:eu-repo/semantics/openAccessADBrain ImagingCNNTransfer LearningVGG-16Master Thesis826407