Abstract
BackgroundAlzheimer’s Disease (AD) is a degenerative brain disorder that often occurs in people over 65 years old. As advanced AD is difficult to manage, accurate diagnosis of the disorder is critical. Previous studies have revealed effective deep learning methods of classification. However, deep learning methods require a large number of image datasets. Moreover, medical images are affected by various environmental factors. In the current study, we propose a deep learning-based method for diagnosis of Alzheimer’s disease (AD) that is less sensitive to different datasets for external validation, based upon F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT).ResultsThe accuracy, sensitivity, and specificity of our proposed network were 86.09%, 80.00%, and 92.96% (respectively) using our dataset, and 91.02%, 87.93%, and 93.57% (respectively) using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. We observed that our model classified AD and normal cognitive (NC) cases based on the posterior cingulate cortex (PCC), where pathological changes occur in AD. The performance of the GAP layer was considered statistically significant compared to the fully connected layer in both datasets for accuracy, sensitivity, and specificity (p < 0.01). In addition, performance comparison between the ADNI dataset and our dataset showed no statistically significant differences in accuracy, sensitivity, and specificity (p > 0.05).ConclusionsThe proposed model demonstrated the effectiveness of AD classification using the GAP layer. Our model learned the AD features from PCC in both the ADNI and Severance datasets, which can be seen in the heatmap. Furthermore, we showed that there were no significant differences in performance using statistical analysis.
Highlights
Alzheimer’s Disease (AD) is a degenerative brain disorder that often occurs in people over 65 years old
The ranges of slice numbers were chosen to include those that cover the locations where neuropathological changes occur in AD
Sensitivity, and specificity were enhanced by 3.34%, 3.75%, and 2.82% with the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and by 2.74%, 2.59%, and 2.86% with our dataset
Summary
Alzheimer’s Disease (AD) is a degenerative brain disorder that often occurs in people over 65 years old. We propose a deep learning-based method for diagnosis of Alzheimer’s disease (AD) that is less sensitive to different datasets for external validation, based upon F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT). Alzheimer’s Disease (AD), characterized by a decline in cognitive function, is one of the most common degenerative brain disorders Because this disease characteristically presents in people over 65 years old, the incidence of AD has increased sharply in concert with the increase of the elderly population. A convolutional neural network (CNN) is a highly effective method of deep learning used to analyze and classify visual images of all kinds that cannot be resolved by conventional machine learning algorithms. Krizhevsky et al [5] reported an error rate of 15.3% at the Image-net Large-Scale Visual Recognition Competition (ILSVRC), significantly lower than the 26.2% error rate reported using the conventional machine learning method. Because the CNN-based method extracts appropriate features learned from images, unlike conventional machine learning algorithms, it does not require domain knowledge to extract Regions of Interest (ROI) or handcrafted features
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