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Deep learning based mild cognitive impairment diagnosis using structure MR images
Jingwan Jiang1, Li Kang1, Jianjun Huang1
1College of Information Engineering, Shenzhen University, Shenzhen 518060, China.
Abstract:
Mild cognitive impairment (MCI) is an early sign of Alzheimer's disease (AD) which is the fourth leading disease mostly found in the aged population. Early intervention of MCI will possibly delay the progress towards AD, and this makes it very important to diagnose early MCI (EMCI). However, it is very difficult since the subtle difference between EMCI and cognitively normal control (NC). For improving classification performance, this paper presents a deep learning based diagnosis approach using structure MRI images for exploiting deeply embedded diagnosis features; then a feature selection strategy is performed to eliminate redundant features. A Support Vector Machine (SVM) is further employed to distinguish EMCI from NC. Experiments were performed on the publicly available ADNI dataset with a total of 120 subjects. The classification results demonstrate the superior performance of the proposed method with accuracy of 89.4% for EMCI versus NC.
Insights
This study introduces a deep learning method for diagnosing early mild cognitive impairment (EMCI), an Alzheimer's disease precursor. The approach accurately distinguishes EMCI from normal cognition using MRI scans, aiding early intervention.
Area of Science:
- Neuroimaging
- Artificial Intelligence in Medicine
- Neurology
Background:
- Mild cognitive impairment (MCI) is an early indicator of Alzheimer's disease (AD), a significant health concern in aging populations.
- Early diagnosis of early MCI (EMCI) is crucial for potential intervention to delay AD progression.
- Distinguishing EMCI from cognitively normal (NC) individuals is challenging due to subtle differences.
Purpose of the Study:
- To develop and evaluate a deep learning-based approach for improved classification of early mild cognitive impairment (EMCI) versus cognitively normal (NC).
- To leverage structural MRI data for extracting deeply embedded diagnostic features.
- To enhance diagnostic accuracy through feature selection and Support Vector Machine (SVM) classification.
Main Methods:
- A deep learning model was employed to analyze structural MRI images for feature extraction.
- A feature selection strategy was implemented to identify and remove redundant features.
- A Support Vector Machine (SVM) classifier was utilized to differentiate between EMCI and NC subjects.
Main Results:
- The proposed deep learning method achieved a high accuracy of 89.4% in distinguishing EMCI from NC.
- Experiments were conducted on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.
- The method demonstrated superior performance in classifying early MCI versus normal controls.
Conclusions:
- The developed deep learning approach shows significant promise for the early and accurate diagnosis of EMCI.
- This method can aid clinicians in identifying individuals at risk for Alzheimer's disease.
- The integration of deep learning with MRI analysis offers a powerful tool for neurodegenerative disease research.
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