This article focuses on the early detection of various stages of cognitive aging and Alzheimer's disease (AD) using neuroimaging and transfer learning (TL). The study utilizes images from the Kaggle Alzheimer's Dataset, which includes four classes: non-dementia (NONDEM), very mild dementia (VERDEM), mild dementia (MILDEM), and moderate dementia (MODDEM). Six pre-trained deep neural networks—VGG-19, VGG-16, ResNet-50, InceptionV3, Xception, and DenseNet169—are compared for their classification performance. Each network was tested on 6400 images, and the confusion matrix was used to evaluate their accuracy. The results show that all six networks achieved high overall precision, with VGG-19 and VGG-16 performing slightly better at 92.86% and 92.83%, respectively. The study highlights the potential of deep learning in overcoming the challenges of large datasets and network complexity, making it a promising tool for early and accurate diagnosis of AD.This article focuses on the early detection of various stages of cognitive aging and Alzheimer's disease (AD) using neuroimaging and transfer learning (TL). The study utilizes images from the Kaggle Alzheimer's Dataset, which includes four classes: non-dementia (NONDEM), very mild dementia (VERDEM), mild dementia (MILDEM), and moderate dementia (MODDEM). Six pre-trained deep neural networks—VGG-19, VGG-16, ResNet-50, InceptionV3, Xception, and DenseNet169—are compared for their classification performance. Each network was tested on 6400 images, and the confusion matrix was used to evaluate their accuracy. The results show that all six networks achieved high overall precision, with VGG-19 and VGG-16 performing slightly better at 92.86% and 92.83%, respectively. The study highlights the potential of deep learning in overcoming the challenges of large datasets and network complexity, making it a promising tool for early and accurate diagnosis of AD.