This paper investigates if the accuracy of diagnosing EMCI could be enhanced using multi-input data (eg, structural Magnetic Resonance Imaging (sMRI), ...
This paper investigates if the accuracy of diagnosing EMCI could be enhanced using multi-input data (eg, structural Magnetic Resonance Imaging (sMRI), ...
By leveraging models based on a shallow CNN and hybrid neural network, this paper investigates if the accuracy of diagnosing. EMCI could be enhanced using multi ...
Early Diagnosis of Mild Cognitive Impairment Using Deep Neural Network with Single and Multi-input Analysis. June 2023. DOI:10.1109/ICHI57859.2023.00048.
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In this study, we used deep learning and machine learning techniques to predict the progression from cognitively unimpaired to MCI and also to analyze the ...
A machine learning approach based on deep neural network (DNN) has been proposed in order to detect AD in its early stage using multimodal imaging, ...
Aug 9, 2022 · An early detection of MCI is a crucial step for timely prevention and intervention. Recent studies have developed deep learning models to detect ...
Dec 2, 2023 · Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks. NeuroImage ...
Mar 27, 2023 · This paper aims to address this gap by proposing the use of structural MRI (sMRI) images and demographic information, in conjunction with predictive models.
Dec 16, 2021 · In this work, we developed a novel multi-input deep learning model that integrates three different drawing tasks to perform an explainable MCI ...