Diagnosis of Alzheimer Stages by Using CNN Architecture

Alzheimer's disease, a widespread neurodegenerative condition predominantly affecting the elderly on a global scale, underscores the critical need for timely and precise diagnosis to improve intervention and patient care. This study introduces a pioneering methodology for diagnosing and staging Alzheimer's, utilizing cutting-edge Convolutional Neural Network (CNN) architectures applied meticulously to brain imaging datasets. The intricately crafted CNN models demonstrate a sophisticated ability to autonomously extract nuanced features from Magnetic Resonance Imaging (MRI) scans. This involves discerning both local and global patterns intricately associated with the disease's progression. Through leveraging advanced machine learning techniques, our approach aims to improve the accuracy of Alzheimer's diagnosis, enabling personalized and targeted treatment strategies. Validation of our method entails a comprehensive evaluation using an impartial test dataset. Thorough analysis is done on strict performance parameters, such as the Area Under the Receiver Operating Characteristic Curve (AUC-ROC), sensitivity, specificity, and accuracy. These metrics serve as robust indicators of the diagnostic and staging capabilities inherent in the CNN models, ensuring the reliability and effectiveness of our innovative approach.