Deep Multi-Branch CNN Architecture for Early Alzheimer's Detection from Brain MRIs

TL;DR

This paper introduces a novel deep multi-branch CNN architecture for early Alzheimer's detection from brain MRIs, achieving high accuracy and offering a comprehensive review of existing methods.

Contribution

The paper presents a new multi-branch CNN model with three different kernel sizes for improved early AD classification from MRI data.

Findings

Achieved 99.05% accuracy in three-class AD diagnosis

Proposed a CNN with over 7.8 million parameters and three convolutional branches

Provided a review of existing early detection approaches

Abstract

Alzheimer's disease (AD) is a neuro-degenerative disease that can cause dementia and result severe reduction in brain function inhibiting simple tasks especially if no preventative care is taken. Over 1 in 9 Americans suffer from AD induced dementia and unpaid care for people with AD related dementia is valued at $271.6 billion. Hence, various approaches have been developed for early AD diagnosis to prevent its further progression. In this paper, we first review other approaches that could be used for early detection of AD. We then give an overview of our dataset that was from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and propose a deep Convolutional Neural Network (CNN) architecture consisting of 7,866,819 parameters. This model has three different convolutional branches with each having a different length. Each branch is comprised of different kernel sizes. This model can predict whether a patient is non-demented, mild-demented, or moderately demented with a 99.05% three class accuracy.