MRI Images Analysis Method for Early Stage Alzheimer's Disease Detection
This research aims to improve early diagnosis of Alzheimer's disease for patients, which is crucial for timely intervention, though the method appears to be an incremental application of existing deep learning techniques.
This paper addresses the challenging problem of early-stage Alzheimer's disease detection by identifying Mild Cognitive Impairment (MCI) from MRI images. The proposed classification architecture, utilizing a pre-trained AlexNet for feature extraction, achieved an accuracy of 96.83% on a dataset of 420 subjects from the OASIS Database Brain.
Alzheimer's disease is a neurogenerative disease that alters memories, cognitive functions leading to death. Early diagnosis of the disease, by detection of the preliminary stage, called Mild Cognitive Impairment (MCI), remains a challenging issue. In this respect, we introduce, in this paper, a powerful classification architecture that implements the pre-trained network AlexNet to automatically extract the most prominent features from Magnetic Resonance Imaging (MRI) images in order to detect the Alzheimer's disease at the MCI stage. The proposed method is evaluated using a big database from OASIS Database Brain. Various sections of the brain: frontal, sagittal and axial were used. The proposed method achieved 96.83% accuracy by using 420 subjects: 210 Normal and 210 MRI