Design of a deep learning prediction model for Alzheimer's and Parkinson's Disease using MRI images.
The paper presents a high-accuracy (97.4%) deep-learning pipeline—using InceptionGAN augmentation, ConvNeXt and MaxViT feature extractors with Cross-Fusion Attention and hyperparameter optimization (Bayesian + genetic algorithms)—for classifying Alzheimer's and Parkinson's disease from brain MRI…
What the AI sees
The paper presents a high-accuracy (97.4%) deep-learning pipeline—using InceptionGAN augmentation, ConvNeXt and MaxViT feature extractors with Cross-Fusion Attention and hyperparameter optimization (Bayesian + genetic algorithms)—for classifying Alzheimer's and Parkinson's disease from brain MRI…
Research significance
This work could improve MRI-based diagnosis and patient stratification for clinical studies, but it provides little actionable biological insight or direct therapeutic targets for Parkinson's disease drug discovery.
Source abstract
INTRODUCTION: Alzheimer's disease (AD) and Parkinson's disease (PD) are types of neurodegenerative diseases that affect the body and get worse over time. The cause of AD mainly involves the buildup of protein which are abnormal, issues with the immune reaction, death of neurons. Different from this, the death of the neurons that make dopamine leads to PD and causes both motor and non-motor problems. MRI images are used to provide an early and correct diagnosis to enable timely treatment planning and management of the disease. METHODS: In this paper, a design of an AI-based deep learning framework is proposed for the classification of neurodegenerative disease based on the brain MRI data. The pipeline that we propose begins with data preparation including data augmentation using InceptionGAN for augmentation of the dataset and fixing of class imbalance issues. A composite method of feature extraction using ConvNeXt and MaxViT along with the Cross-Fusion Attention model, worked well to capture local and global spatial features. Bayesian Optimization and Genetic Algorithm are used to optimize hyperparameters for improving the performance of the model. RESULTS: The Hybrid Deep Neural Network (HDNN) is the last classifier with an accuracy of 97.4%. Based on performance accuracy, F1-score, the model is strong and reliable. We used Gradient-weighted Class Activation Mapping++ to explain how regions of interest in the brain influence our model's decisions. DISCUSSION: This study offers an interpretable and high-performing deep learning framework for the early and precise prediction of neurodegenerative disorders utilizing MRI imaging, thereby enhancing clinical decision-making and patient care.