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

Jun 6, 2020International journal of neural systems

Automated MRI Deep Learning Model to Detect Alzheimer's Disease

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Abstract

The 3D-CNN-SVM achieved a ternary classification accuracy of 96.82% for mild cognitive impairment diagnoses.

Three-dimensional convolutional neural networks (3D-CNNs) were used alongside magnetic resonance imaging (MRI) for classifying Alzheimer's disease (AD) and mild cognitive impairment (MCI).
The 3D-CNN-SVM model outperformed other deep learning methods in both ternary and binary classifications.
For binary classification, the 3D-CNN-SVM achieved high accuracy, sensitivity, and specificity in distinguishing between normal controls (NC), MCI, and AD.
The model's performance included accuracy rates of 98.90% for NC versus MCI, 99.10% for NC versus AD, and 89.40% for MCI versus AD.
3D-CNN-SVM demonstrated efficiency without the need for manual feature extraction, making it accessible for untrained operators.
Due to its noninvasive nature, 3D-CNN-SVM may serve as a viable screening tool for Alzheimer's disease in the general population.

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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.

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