Classification and Visualization of Alzheimer’s Disease using Volumetric Convolutional Neural Network and Transfer Learning

Abstract

Recently, deep-learning-based approaches have been proposed for the classification of neuroimaging data related to Alzheimer’s disease (AD), and significant progress has been made. However, end-to-end learning that is capable of maximizing the impact of deep learning has yet to receive much attention due to the endemic challenge of neuroimaging caused by the scarcity of data. Thus, this study presents an approach meant to encourage the end-to-end learning of a volumetric convolutional neural network (CNN) model for four binary classification tasks (AD vs. normal control (NC), progressive mild cognitive impairment (pMCI) vs. NC, stable mild cognitive impairment (sMCI) vs. NC and pMCI vs. sMCI) based on magnetic resonance imaging (MRI) and visualizes its outcomes in terms of the decision of the CNNs without any human intervention. In the proposed approach, we use convolutional autoencoder (CAE)-based unsupervised learning for the AD vs. NC classification task, and supervised transfer learning is applied to solve the pMCI vs. sMCI classification task. To detect the most important biomarkers related to AD and pMCI, a gradient-based visualization method that approximates the spatial influence of the CNN model’s decision was applied. To validate the contributions of this study, we conducted experiments on the ADNI database, and the results demonstrated that the proposed approach achieved the accuracies of 86.60% and 73.95% for the AD and pMCI classification tasks respectively, outperforming other network models. In the visualization results, the temporal and parietal lobes were identified as key regions for classification.

Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG

“…Therefore, a nCV approach has been employed here to facilitate HP optimization and a more reliable estimation of performance. Adapted from principles described in [66,67], the nCV method depicted in Algorithm 1 has been specifically tailored for DL validation. Firstly, the data are split into k folds, one of which is retained in the outer fold for testing.…”

Detection of Parkinson’s Disease from 3T T1 Weighted MRI Scans Using 3D Convolutional Neural Network

“…Also, Hosseini et al [15] undertook a study for the classification of AD, HC, and MCI by leveraging 3D convolutional autoencoder on normalized T1 weighted MRI scans from the ADNI database. Similarly, others have also demonstrated their works where CNN was leveraged in MRI and Functional magnetic resonance imaging (fMRI) scans for the detection of AD and the classification of MCI and HC [16][17][18][19][20].…”

Using text and acoustic features to diagnose Mild Cognitive Impairment and Alzheimer’s disease

“…With the development of AI technology, more and more researcher join in this eld, now the accuracy of deep learning in this area has been about 90% [20]- [23] in recent two years, the result of which is better than traditional machine learning algorithm, the automatic feature extraction algorithm, such as CNN (convolutional neural network), RNN(Recurrent Neural Network), Transformer, Bert and so on, can capture more subtle linguistic markers especially for MCI. In the future we hope a better classify performance by combining automatic feature extraction method with deep learning algorithm and manual feature extraction with clinical discourse analysis based on the theoretical framework of systemic functional linguistics.…”