Deep Multi-Branch CNN Architecture for Early Alzheimer's Detection from Brain MRIs
This work addresses early diagnosis of Alzheimer's disease, a critical issue for patients and caregivers, but it appears incremental as it builds on existing CNN methods for medical imaging.
The paper tackles early Alzheimer's detection from brain MRIs by proposing a deep multi-branch CNN architecture, achieving a 99.05% three-class accuracy in classifying patients as non-demented, mild-demented, or moderately demented.
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.