An Optimized Deep Learning Model for Predicting Mild Cognitive Impairment Using Structural MRI

Early diagnosis of mild cognitive impairment (MCI) with magnetic resonance imaging (MRI) has been shown to positively affect patients' lives. To save time and costs associated with clinical investigation, deep learning approaches have been used widely to predict MCI. This study proposes optimiz...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-06, Vol.23 (12), p.5648
Main Authors: Alyoubi, Esraa H, Moria, Kawthar M, Alghamdi, Jamaan S, Tayeb, Haythum O
Format: Article
Language:English
Subjects:
Alzheimer's disease
Anniversaries
Atrophy
Biomarkers
Brain
Brain research
Classification
Computer architecture
Deep learning
entorhinal cortex
Hippocampus
Impairment
Magnetic resonance imaging
mild cognitive impairments
Neural networks
Pharmaceutical industry
transfer learning
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Summary:Early diagnosis of mild cognitive impairment (MCI) with magnetic resonance imaging (MRI) has been shown to positively affect patients' lives. To save time and costs associated with clinical investigation, deep learning approaches have been used widely to predict MCI. This study proposes optimized deep learning models for differentiating between MCI and normal control samples. In previous studies, the hippocampus region located in the brain is used extensively to diagnose MCI. The entorhinal cortex is a promising area for diagnosing MCI since severe atrophy is observed when diagnosing the disease before the shrinkage of the hippocampus. Due to the small size of the entorhinal cortex area relative to the hippocampus, limited research has been conducted on the entorhinal cortex brain region for predicting MCI. This study involves the construction of a dataset containing only the entorhinal cortex area to implement the classification system. To extract the features of the entorhinal cortex area, three different neural network architectures are optimized independently: VGG16, Inception-V3, and ResNet50. The best outcomes were achieved utilizing the convolution neural network classifier and the Inception-V3 architecture for feature extraction, with accuracy, sensitivity, specificity, and area under the curve scores of 70%, 90%, 54%, and 69%, respectively. Furthermore, the model has an acceptable balance between precision and recall, achieving an F1 score of 73%. The results of this study validate the effectiveness of our approach in predicting MCI and may contribute to diagnosing MCI through MRI.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23125648