scholarly journals Multi-classification of alzheimer disease on magnetic resonance images (MRI) using deep convolutional neural network (DCNN) approaches

2021 ◽  
Vol 11 (53) ◽  
pp. 51-60
Author(s):  
Sunday Adeola Ajagbe ◽  
Kamorudeen A. Amuda ◽  
Matthew A. Oladipupo ◽  
Oluwaseyi F. AFE ◽  
Kikelomo I. Okesola
Keyword(s):  
Neural Network ◽  
Magnetic Resonance ◽  
Alzheimer Disease ◽  
Convolutional Neural Network ◽  
Magnetic Resonance Images ◽  
Deep Convolutional Neural Network ◽  
Multi Classification

Deep Convolutional Neural Network for Compressive Sensing of Magnetic Resonance Images

2021 ◽  
Author(s):  
Hong Lu ◽  
Xiaofei Zou ◽  
Longlong Liao ◽  
Kenli Li ◽  
Jie Liu
Keyword(s):  
Neural Network ◽  
Magnetic Resonance ◽  
Convolutional Neural Network ◽  
Compressive Sensing ◽  
Network Architecture ◽  
Structural Similarity ◽  
Magnetic Resonance Images ◽  
Deep Convolutional Neural Network ◽  
Mr Images ◽  
Under Sampling

Compressive Sensing for Magnetic Resonance Imaging (CS-MRI) aims to reconstruct Magnetic Resonance (MR) images from under-sampled raw data. There are two challenges to improve CS-MRI methods, i.e. designing an under-sampling algorithm to achieve optimal sampling, as well as designing fast and small deep neural networks to obtain reconstructed MR images with superior quality. To improve the reconstruction quality of MR images, we propose a novel deep convolutional neural network architecture for CS-MRI named MRCSNet. The MRCSNet consists of three sub-networks, a compressive sensing sampling sub-network, an initial reconstruction sub-network, and a refined reconstruction sub-network. Experimental results demonstrate that MRCSNet generates high-quality reconstructed MR images at various under-sampling ratios, and also meets the requirements of real-time CS-MRI applications. Compared to state-of-the-art CS-MRI approaches, MRCSNet offers a significant improvement in reconstruction accuracies, such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). Besides, it reduces the reconstruction error evaluated by the Normalized Root-Mean-Square Error (NRMSE). The source codes are available at https://github.com/TaihuLight/MRCSNet .

scholarly journals Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242712
Author(s):  
Samsuddin Ahmed ◽  
Byeong C. Kim ◽  
Kun Ho Lee ◽  
Ho Yub Jung ◽  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance ◽  
Alzheimer Disease ◽  
Convolutional Neural Network ◽  
Region Of Interest ◽  
Volumetric Analysis ◽  
Ensemble Classifier ◽  
Magnetic Resonance Images ◽  
Disease Spectrum ◽  
Preclinical Ad

Patches from three orthogonal views of selected cerebral regions can be utilized to learn convolutional neural network (CNN) models for staging the Alzheimer disease (AD) spectrum including preclinical AD, mild cognitive impairment due to AD, and dementia due to AD and normal controls. Hippocampi, amygdalae and insulae were selected from the volumetric analysis of structured magnetic resonance images (MRIs). Three-view patches (TVPs) from these regions were fed to the CNN for training. MRIs were classified with the SoftMax-normalized scores of individual model predictions on TVPs. The significance of each region of interest (ROI) for staging the AD spectrum was evaluated and reported. The results of the ensemble classifier are compared with state-of-the-art methods using the same evaluation metrics. Patch-based ROI ensembles provide comparable diagnostic performance for AD staging. In this work, TVP-based ROI analysis using a CNN provides informative landmarks in cerebral MRIs and may have significance in clinical studies and computer-aided diagnosis system design.

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