Transfer Learning for Detecting Alzheimer’s Disease in Brain Using Magnetic Resonance Images - Islam, Md. Monirul; Uddin, Jia (Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 2025)
Alzheimer’s Disease (AD) is one of the most concerning diseases because the patients show very few symptoms at the earlier stages. Dementia is very common in patients who have suffered brain damage or those who have suffered from psychotic trauma. Patients who have a lot of age suffer the most from it. Magnetic resonance imaging (MRI) is widely used to clinically treat patients with Alzheimer’s. Currently, there is no known remedy for the disease. We can only identify and try to give the proper medications to give some relief to patients. In this study, we have collected MRI data from patients with 4 different stages of Alzheimer’s. The purpose of this paper is to build a model to securely detect these stages for the betterment of medical science. We implemented a transfer learning method with state-of-the-art models such as ResNet50, DenseNet121, and VGG19. We proposed our method with these models which have pre-trained weights of “ImageNet”. The layers that we added are our novelty. We were able to achieve 97.70% accuracy on our best pre-trained model with an F1 score of 97% and a precision of 97% on our test data.