Deep Learning-Based Severity Detection in Alzheimer's Disease: A Comprehensive Study on Cognitive Impairment

Alzheimer's disease is a neurodegenerative disorder affecting millions worldwide, causing cognitive decline. Early detection is crucial for effective treatment, but manual diagnosis is error-prone and time-consuming. This study proposes a deep learning-based model for diagnosing and classifying Alzheimer's disease using VGG-16 CNN architectures. The model uses the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, which includes MRI scans from individuals with Alzheimer's and healthy controls. The model uses a pre-trained VGG-16 convolutional neural network as the base, fine-tuning on the dataset, and data augmentation techniques. The trained model achieves a 96.75% accuracy on the testing dataset, distinguishing MRI scans between Alzheimer's patients and healthy controls. The VGG-16 architecture demonstrated superior performance during training and testing.