Utilizing Convolutional Neural Networks and Ridge Regression for Alzheimer’s Disease Detection

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that impairs cognitive function, leading to dementia. Early diagnosis and intervention are critical to managing the illness progression and improving patients’ life. Interpreting MRI data manually can be a labor-intensive process and is susceptible to errors. We have developed a convolutional neural network (CNN) with ridge regression to enhance the precision and effectiveness of disease detection, and diagnosis through brain MRI imaging is a significant achievement. Our model assesses dementia across four distinct levels delivering highly accurate predictions. It has yielded impressive results, boasting a remarkable accuracy of 99.84%, a minimal loss of 1.44%, and an outstanding Area Under the Curve (AUC) score of 99.93%.We used significantly fewer parameters (2.14 million) compared to existing models. This capability is pivotal in ensuring that individuals receive timely and effective treatment and management for this condition.