article · International Journal of Neural Systems
Deep learning models were evaluated using magnetic resonance imaging to improve the diagnosis of Alzheimer's disease and mild cognitive impairment. Using data from the Alzheimer's Disease Neuroimaging Initiative, the investigation compared two-dimensional convolutional neural networks, three-dimensional convolutional neural networks, and a hybrid model combining three-dimensional networks with support vector machines. The hybrid model produced the highest performance across both binary and ternary diagnostic tasks, accurately distinguishing between normal controls, mild cognitive impairment, and Alzheimer's disease. The system operates without the need for prior manual feature extraction and functions independently of variations in imaging protocols and scanner hardware. Because magnetic resonance imaging is non-invasive and safe, this automated framework offers potential for wider disease screening and clinical patient management.
Accurate and early identification of Alzheimer's disease and mild cognitive impairment is vital for effective patient care. By automating magnetic resonance imaging analysis without requiring manual feature engineering, this approach reduces reliance on specialist expertise and adapts across different scanner types. This supports the development of safer, non-invasive screening options for the general population.
This applied and tested diagnostic software could enable automated screening tools for healthcare providers and radiology departments. The abstract notes that the tool requires no manual feature extraction and functions across different scanner protocols, suggesting suitability for clinical decision-support systems used by non-specialists. Having been evaluated on the ADNI research dataset, it remains in the translational research phase rather than being an immediate near-market product.
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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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DOI: 10.1142/s012906572050032x
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