Early Detection of Alzheimer’s Disease based on the State-Of-The-Art Deep Learning Approach

In this paper, we introduce a deep convolutional neural network architecture, code-named Inception, which sets a new benchmark for classification and detection tasks. Early detection of Alzheimer's disease (AD) is critical, as the brain is the primary organ affected, and early intervention can help slow disease progression. Traditionally, AD diagnosis relies heavily on MRI scans, a process that is both time-consuming and resource-intensive. However, recent advancements in artificial intelligence (AI) present promising solutions for more efficient detection. This study examines the application of various Convolutional Neural Network (CNN) models for detecting AD in MRI images. A comprehensive comparison was performed on four distinct CNN architectures—Inception-V3, ResNet-152, VGG16, and VGG19—using MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The images were categorized into four classes: Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented.While convolutional neural networks (CNNs) excel at identifying advanced dementia stages (Stages 5-7), accurately classifying the earliest, most elusive stages (Stages 2-4) remains a significant challenge. This paper tackles this crucial gap by systematically comparing various CNN models to achieve promising classification accuracy for these crucial early stages of dementia. Through rigorous analysis, we aim to advance the field of dementia detection by enabling earlier diagnosis and intervention, ultimately improving patient outcomes. Therefore, the application of machine learning technology can assist healthcare professionals in enhancing their efforts to combat Alzheimer's, a serious medical condition.