Early-Stage Alzheimer’s Disease Categorization Using PET Neuroimaging Modality and Convolutional Neural Networks in the 2D and 3D Domains

Abstract

Alzheimer’s Disease (AD) is a health apprehension of significant proportions that is negatively impacting the ageing population globally. It is characterized by neuronal loss and the formation of structures such as neurofibrillary tangles and amyloid plaques in the early as well as later stages of the disease. Neuroimaging modalities are routinely used in clinical practice to capture brain alterations associated with AD. On the other hand, deep learning methods are routinely used to recognize patterns in underlying data distributions effectively. This work uses Convolutional Neural Network (CNN) architectures in both 2D and 3D domains to classify the initial stages of AD into AD, Mild Cognitive Impairment (MCI) and Normal Control (NC) classes using the positron emission tomography neuroimaging modality deploying data augmentation in a random zoomed in/out scheme. We used novel concepts such as the blurring before subsampling principle and distant domain transfer learning to build 2D CNN architectures. We performed three binaries, that is, AD/NC, AD/MCI, MCI/NC and one multiclass classification task AD/NC/MCI. The statistical comparison revealed that 3D-CNN architecture performed the best achieving an accuracy of 89.21% on AD/NC, 71.70% on AD/MCI, 62.25% on NC/MCI and 59.73% on AD/NC/MCI classification tasks using a five-fold cross-validation hyperparameter selection approach. Data augmentation helps in achieving superior performance on the multiclass classification task. The obtained results support the application of deep learning models towards early recognition of AD.

A novel approach to perform linear discriminant analyses for a 4-way alzheimer’s disease diagnosis based on an integration of pearson’s correlation coefficients and empirical cumulative distribution function

“…To the best of the authors' knowledge, the Diffusion-weighted imaging modality has not yet been established whether for three or 4-way disease diagnosis. Inspecting previous studies [5,12,16] on this matter, we notice that the multiclass diagnosis was dissected to a binary classification issue, which decreases the reliability and stringency of proposed approaches in the clinical assessment. Furthermore, regarding other imaging modalities, a lack of research concerning four classes of discrimination has also been found.…”

Automatic Analysis of MRI Images for Early Prediction of Alzheimer’s Disease Stages Based on Hybrid Features of CNN and Handcrafted Features

“…Second, the p-step, which determines the number of steps the filter slides over the image. Third, the zero-padding of the image edges, which preserves the original image size during the processing process [37]:…”

Development and Validation of Embedded Device for Electrocardiogram Arrhythmia Empowered with Transfer Learning

“…e algorithm outperformed the best architectures currently on the market with a sensitivity of 96.67%. Performance comparison of 2D and 3D CNN architectures is done for early Alzheimer's Computational Intelligence and Neuroscience disease symptom detection [41]. We divided people into the four groups of Alzheimer's disease (AD), non-Alzheimer's disease (NC), mild cognitive impairment (MCI), and AD using a five-fold CV method for selecting hyperparameters.…”