A Dimension Centric Proximate Attention Network and Swin Transformer for Age-Based Classification of Mild Cognitive Impairment From Brain MRI

Abstract

The early identification and treatment of Mild Cognitive Impairment (MCI) play a crucial role in managing the risk of Alzheimer's disease (AD). However, current methods for categorizing progressive MCI and stable MCI based on brain MRI scans have proven insufficient due to the subtle nature of the features involved. This research aims to improve the effectiveness of MCI classification through the utilization of a Deep Learning (DL) network. The primary objective of this work is to improve the feature representation of brain MRI scans for more accurate classification. The proposed model is a hybrid MCI classification system that integrates three components: the Swin Transformer, the Dimension Centric Proximity Aware Attention Network (DCPAN), and the Age Deviation Factor (ADF). The proposed network achieves better classification results through a unique feature fusion approach that combines global, local, proximal features, and dimensional dependencies. It effectively combines fine-grained details with broader contextual information to extract discriminative features. Experimental results demonstrate the effectiveness of the proposed network, achieving an accuracy of 79.8%, precision of 76.6%, recall of 80.2%, and an F1-score of 78.4% when evaluated on the ADNI dataset.

Enhancing Alzheimer’s disease diagnosis and staging: a multistage CNN framework using MRI

“…They achieved an accuracy of 93.5% for the two classes using their proposed model. Illakiya et al (36) utilized a swine transformer, a dimension-centric proximity-aware attention network, and an age deviation factor to improve feature extraction from brain MRI images. The proposed network improves the classification results by utilizing a novel feature fusion strategy that incorporates global, local, and proximal characteristics, as well as dimensional dependencies.…”