Alzheimer's disease is a neurodegenerative ailment that is defined by a gradual deterioration in cognitive and memory abilities, as well as that of the capacity to carry out fundamental activities. Symptoms typically manifest in later years, affecting individuals of all age groups, particularly older adults. Timely identification is essential for accurate diagnosis of this condition. Manual diagnosis by healthcare professionals is often time-consuming and prone to errors due to the widespread prevalence of the disease. Recent advancements in information technology, including deep learning, machine learning, and artificial intelligence, have enabled the development of autonomous systems that require minimal human intervention. This study utilizes deep learning techniques in combination with image processing methods for the detection of Alzheimer's disease. A novel approach to detect and classify Alzheimer's disease is proposed and evaluated using a real dataset obtained from the ADNI repository on Kaggle. The dataset classifies images into five distinct categories: cognitive normal, mild cognitive impairment, early mild cognitive impairment, late mild cognitive impairment, and Alzheimer's disease. Three deep learning models, namely DenseNet, Inception V3, and ResNet 50, are implemented and assessed. The results indicate that the proposed models effectively identify Alzheimer's disease at all stages. Specifically, ResNet 50 demonstrates superior accuracy in detecting Alzheimer's disease and cognitive normal classes, while DenseNet exhibits stronger performance in detecting mild cognitive impairment in its early and late stages.