Accurate Detection of Alzheimer's Disease Using Lightweight Deep Learning Model on MRI Data.

Area of Science:

• Neuroscience
• Artificial Intelligence
• Medical Imaging

Background:

• Alzheimer's disease (AD) is a neurodegenerative disorder requiring early detection for effective treatment.
• Deep learning models have shown promise in analyzing medical images for AD detection.

Purpose of the Study:

• To propose an improved, lightweight deep learning model for accurate Alzheimer's disease detection from MRI images.
• To enhance diagnostic efficiency by reducing model complexity and processing time.

Main Methods:

• Developed a novel, lightweight deep learning model with seven layers for AD detection.
• Integrated feature extraction and classification into a single stage, eliminating traditional methods.
• Evaluated the model on a publicly available Kaggle dataset.

Main Results:

• Achieved 99.22% accuracy for binary classification and 95.93% for multi-classification tasks.
• Outperformed previous models in AD detection accuracy.
• Demonstrated a less complex and time-efficient processing system.

Conclusions:

• The proposed lightweight deep learning framework effectively achieves high accuracy in Alzheimer's disease classification.
• This model offers a promising, efficient tool for early AD detection using MRI.
• The study introduces a novel approach to AD detection on a challenging, integrated dataset.