Enhancing Alzheimer's Disease Detection Using Advanced MRI Preprocessing and Deep Learning Techniques
Corresponding author: Gandla Shivakanth
Abstract
This study investigates how optimized Magnetic Resonance Imaging (MRI) preprocessing enhances the performance of Artificial Intelligence (AI) models for Alzheimer's Disease (AD) detection. A standardized preprocessing pipeline, comprising noise reduction, skull stripping, and intensity normalization, was integrated into a hybrid Deep Learning (DL) model called MRI-Net. MRI-Net combines Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Vision Transformers (ViTs) to leverage complementary spatial and contextual information. Using the publicly available ADNI dataset, MRI-Net achieved a classification accuracy of 94.7%, surpassing baseline CNN (88.2%), CNN+RNN (90.5%), and ViT (92.8%) models. The inclusion of a structured preprocessing pipeline improved anatomical clarity, reduced inter-subject variability, and enhanced feature discriminability. These findings demonstrate that optimized preprocessing substantially strengthens model generalization and diagnostic reliability. The proposed MRI-Net framework highlights the importance of integrating preprocessing and hybrid AI architectures to support early-stage AD detection and clinical decision-making.
Keywords:
Alzheimer's disease detection, MRI preprocessing, machine learning, deep learning, feature engineering, biomarkers, medical imaging, diagnostic accuracyDownloads
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Copyright (c) 2026 Bandaru A. Chakravarthi, Gandla Shivakanth
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