Automated MRI-Based Deep Learning Model for Detection of Alzheimer’s Disease Process

Abstract

In the context of neuro-pathological disorders, neuroimaging has been widely accepted as a clinical tool for diagnosing patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI). The advanced deep learning method, a novel brain imaging technique, was applied in this study to evaluate its contribution to improving the diagnostic accuracy of AD. Three-dimensional convolutional neural networks (3D-CNNs) were applied with magnetic resonance imaging (MRI) to execute binary and ternary disease classification models. The dataset from the Alzheimer’s disease neuroimaging initiative (ADNI) was used to compare the deep learning performances across 3D-CNN, 3D-CNN-support vector machine (SVM) and two-dimensional (2D)-CNN models. The outcomes of accuracy with ternary classification for 2D-CNN, 3D-CNN and 3D-CNN-SVM were [Formula: see text]%, [Formula: see text]% and [Formula: see text]% respectively. The 3D-CNN-SVM yielded a ternary classification accuracy of 93.71%, 96.82% and 96.73% for NC, MCI and AD diagnoses, respectively. Furthermore, 3D-CNN-SVM showed the best performance for binary classification. Our study indicated that ‘NC versus MCI’ showed accuracy, sensitivity and specificity of 98.90%, 98.90% and 98.80%; ‘NC versus AD’ showed accuracy, sensitivity and specificity of 99.10%, 99.80% and 98.40%; and ‘MCI versus AD’ showed accuracy, sensitivity and specificity of 89.40%, 86.70% and 84.00%, respectively. This study clearly demonstrates that 3D-CNN-SVM yields better performance with MRI compared to currently utilized deep learning methods. In addition, 3D-CNN-SVM proved to be efficient without having to manually perform any prior feature extraction and is totally independent of the variability of imaging protocols and scanners. This suggests that it can potentially be exploited by untrained operators and extended to virtual patient imaging data. Furthermore, owing to the safety, noninvasiveness and nonirradiative properties of the MRI modality, 3D-CNN-SMV may serve as an effective screening option for AD in the general population. This study holds value in distinguishing AD and MCI subjects from normal controls and to improve value-based care of patients in clinical practice.

Systemic Review on Transcranial Electrical Stimulation Parameters and EEG/fNIRS Features for Brain Diseases

“…Approximately 60-70% of dementia is caused by AD. AD is a progressive brain disorder in which memory and cognitive function cause increasing impairment until death (Vuksanović et al, 2019;Feng et al, 2020). To date, there is no pharmacological cure available, except for treatment to manage symptoms.…”

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

“…The model was trained using AD and NC subjects from ADNI and validated on three different databases such as AIBL, FHS, and NACC. The superiority of the 3D-CNN-SVM model as compared to the other reported classification models illustrated that the DL model has great potential for medical diagnostics [100]. A multi-modal DL approach exploiting hybrid CNN and DBN was investigated in [101].…”

Impact of EEG Parameters Detecting Dementia Diseases: A Systematic Review

“…CNN is a variation on the multilayer perceptron, uses two-dimensional matrices and is very effective in classification, computer vision and image segmentation applications. [104], [105] an example of the application of CNN in EEG signals is described.…”