Abstract
Alzheimer's disease, a debilitating neurodegenerative condition, poses a formidable challenge in healthcare, necessitating early and precise detection for effective intervention. This study delves into the realm of Alzheimer's detection, leveraging the prowess of deep learning. A novel convolutional neural network (CNN) model is proposed for AD detection. This model achieves a remarkable accuracy of 99.99% on the test dataset, outperforming established pre-trained models like ResNet50, DenseNet201, and VGG16. The outcomes distinctly highlight the CNN model's superior precision in AD identification, marking a watershed moment in neurodegenerative disease detection. The findings of this research have important implications for the development of a more accurate and sensitive diagnostic tool, which could lead to significant advancements in the early diagnosis and treatment of Alzheimer's disease. This research not only presents a novel diagnostic approach for AD but also demonstrates its resilience and potential for accurate classification of early Alzheimer's disease diagnosis.