Alzheimer’s disease, a neurodegenerative condition severely impacting cognition and memory, stands as a predominant contributor to dementia. This chapter addresses the urgent need for effective identification and categorization of Alzheimer’s Disease (AD) dementia, crucial for timely intervention and improved patient outcomes. This chapter introduces a novel Squeeze and Excitation ResNet-152 (or SE-ResNet-152) model based on Convolutional Neural Network (CNN), for the classification of Alzheimer’s disease dementia into four distinct categories namely Very Mild Dementia (VMD), Mild Dementia (MD), Moderate Dementia (MoD), and Non-demented (ND), utilizing Magnetic Resonance Imaging (MRI) images. The incorporation of the Squeeze and Excitation (SE) block plays a pivotal role by recalibrating channel-wise feature responses, thereby boosting the model’s capacity to capture informative features from input data. The SE-ResNet-152 model exhibits an impressive overall accuracy of 99.00%, emphasizing its potential as a powerful tool for accurate Alzheimer’s disease dementia classification and the subsequent facilitation of timely interventions.