Leveraging Bi-Focal Perspectives and Granular Feature Integration for Accurate Reliable Early Alzheimer's Detection

TL;DR

This paper introduces a novel deep learning approach combining granular feature integration and bi-focal perspectives to improve early Alzheimer's detection from MRI scans, achieving near-perfect classification metrics.

Contribution

The authors propose a new model that captures multi-scale features and emphasizes subtle pathological markers, outperforming existing CNN-based methods in AD diagnosis.

Findings

Achieved an F1-Score of 99.31%

Achieved a precision of 99.24%

Achieved a recall of 99.51%

Abstract

Being the most commonly known neurodegeneration, Alzheimer's Disease (AD) is annually diagnosed in millions of patients. The present medical scenario still finds the exact diagnosis and classification of AD through neuroimaging data as a challenging task. Traditional CNNs can extract a good amount of low-level information in an image while failing to extract high-level minuscule particles, which is a significant challenge in detecting AD from MRI scans. To overcome this, we propose a novel Granular Feature Integration method to combine information extraction at different scales along with an efficient information flow, enabling the model to capture both broad and fine-grained features simultaneously. We also propose a Bi-Focal Perspective mechanism to highlight the subtle neurofibrillary tangles and amyloid plaques in the MRI scans, ensuring that critical pathological markers are accurately identified. Our model achieved an F1-Score of 99.31%, precision of 99.24%, and recall of 99.51%. These scores prove that our model is significantly better than the state-of-the-art (SOTA) CNNs in existence.