IHDNA: Identical Hybrid Deep Neural Networks for Alzheimer's Detection using MRI Dataset

Abstract

It has been ascertained in recent studies that AD (Alzheimer's disease, a neurodegenerative disorder) and its earlier stages can be detected by neuroimaging biomarkers. To the prevention, previous studies have focused on volumetric asymmetry and brain atrophy. The identification of AD in its early stages has proven to be imperative, as Alzheimer's disease cannot be cured and can only slow its progression. Developing on that idea, this study aims to use the discriminative powers of a Siamese-inspired identical hybrid neural network for the task of classification between multiple stages. The proposed method uses a homemade pipeline for preprocessing and removing other unwanted components from the MRIs that might disturb the model. Registering all the MRI images to MNI space and resampling the slices helped in normalizing the whole dataset. This study uses feature-based methods to work with low-dimensional characteristics rather than high-dimensional voxel data can lessen computing cost and time spent. We used VGG-16 style net with image-net weights for the purpose of automatic feature extraction. T1-weighted MRIs were used for the research, which were accessed from the ADNI datasets ADNI2 and ADNI3. When compared to a normal DNN, our proposed identical hybrid neural network achieved better precision and F1-score.