NEUROIMAGING-BASED MCI PREDICTION USING DEEP LEARNING
TL;DR
An improved model based on the Xception architecture is created, building on previous deep learning techniques that use convolutional neural networks (CNNs) with channel attention mechanisms, to offer a dependable, accessible diagnostic tool to assist medical professionals in the early detection of Alzheimer's disease.
Abstract
For prompt management and better patient outcomes, early and precise diagnosis of Alzheimer's disease (AD), especially the shift from Cognitively Normal (CN) to Mild Cognitive Impairment (MCI), is essential. This study creates an improved model based on the Xception architecture, building on previous deep learning techniques that use convolutional neural networks (CNNs) with channel attention mechanisms. Both local and global neuroimaging features are captured by the suggested system using sophisticated feature extraction techniques, which are then combined via a learning fusion process. With a binary classification accuracy of 99% for CN versus MCI individuals, our model performs noticeably better than earlier approaches. This enhancement highlights the Xception network's capacity to identify subtle imaging indicators linked to early cognitive deterioration. The system seeks to offer a dependable, accessible diagnostic tool to assist medical professionals in the early detection of Alzheimer's disease, enabling prompt and focused treatment approaches