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Alzheimer's disease is a gradual neurodegenerative condition affecting the brain, causing a decline in cognitive function by progressively damaging nerve cells over time. While a cure for Alzheimer's remains elusive, the detection of Alzheimer's disease (AD) through brain biomarkers is crucial to impede its advancement. High-resolution structural MRI scans, particularly T1-weighted images, are commonly used in Alzheimer's detection. These images provide detailed information about the brain's structure, allowing researchers and clinicians to identify abnormalities. Our study employs a deep learning methodology using T1-weighted MRI images for a binary classification task--distinguishing between AD and normal/healthy control (NC). The volumetric convolutional neural network model is deployed on pre-processed images and validated on MIRIAD datasets, achieving an impressive accuracy of 97%, surpassing other network models. Addressing the challenge of limited datasets for deep learning models, we incorporated various augmentation techniques such as rotation and rescaling, resulting in outstanding model accuracy and effective discerning between Alzheimer's disease and normal controls. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is a chronic brain condition that takes a toll on memory and potential to do even the most basic tasks. With no specific solution viable at this time, it's critical to pinpoint the start of Alzheimer's disease so that necessary steps may be initiated to limit its progression. We used three distinct neuroanatomical computational methodologies namely 3D‐Subject, 3D‐Patches, and 3D‐Slices to construct a multimodal multi‐class deep learning model for three class and two class Alzheimer's classification using T1w‐MRI and AV‐45 PET scans obtained from ADNI database. Further, patches of various sizes were created using the patch‐extraction algorithm designed with torch package leading to separate datasets of patch size 32, 40, 48, 56, 64, 72, 80, and 88. In addition, Slices were produced from images using either uniform slicing, subset slicing, or interpolation zoom approaches then joined back to form a 3D image of varying depth (8,16,24,32,40,48,56, and 64) for the Slice‐based technique. Using T1w‐MRI and AV45‐PET scans, our multimodal multi‐class Ensembled Volumetric ConvNet framework obtained 93.01% accuracy for AD versus NC versus MCI (highest accuracy achieved using multi‐modalities as per our knowledge). The 3D‐Subject‐based neuroanatomy computation approach achieved 93.01% classification accuracy and it overruled Patch‐based approach which achieved 89.55% accuracy and Slice‐Based approach that achieved 89.37% accuracy. Using a 3D‐Patch‐based feature extraction technique, it was discovered that patches of greater size (80, 88) had accuracy over 89%, while medium‐sized patches (56, 64, and 72) had accuracy ranging from 83 to 88%, and small‐sized patches (32, 40, and 48) had the least accuracy ranging from 57 to 80%. From the three independent algorithms created for 3D‐Slice‐based neuroanatomy computational approach, the interpolation zoom technique outperformed uniform slicing and subset slicing, obtaining 89.37% accuracy over 88.35% and 82.83%, respectively. Link to GitHub code: https://github.com/ngoenka04/Alzheimer-Detection. [ABSTRACT FROM AUTHOR]