scholarly journals An Alzheimer’s Disease Identification and Classification Model Based on the Convolutional Neural Network with Attention Mechanisms

2021 ◽  
Vol 38 (5) ◽  
pp. 1557-1564
Author(s):  
Yin Chen
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Brain Mri ◽  
Brain Regions ◽  
Classification Model ◽  
Model Based ◽  
Disease Identification

MRI image analysis of brain regions based on deep learning can effectively reduce the workload of doctors in reading films and improve the accuracy of diagnosis. Therefore, deep learning models have great application prospects in the classification and prediction of Alzheimer’s patients and normal people. However, the existing research has ignored the correlation between small abnormalities in local brain regions and changes in brain tissues. To this end, this paper studies an Alzheimer’s disease identification and classification model based on the convolutional neural network (CNN) with attention mechanisms. In this paper, the attention mechanisms were introduced from the regional level and the feature level, and the information of brain MRI images was fused from multiple levels to find out the correlation between the slices in brain MRI images. Then, a spatio-temporal graph CNN with dual attention mechanisms was constructed, which made the network model more attentive to the salient channel features while eliminating the impact of certain noise features. The experimental results verified the effectiveness of the constructed model in identification and classification of Alzheimer’s disease.

scholarly journals Early Detection of Alzheimer’s Disease using Convolutional Neural Network Architecture

2021 ◽  
Vol 8 (2) ◽  
pp. 48-57
Author(s):  
Deepthi Kamath ◽  
Misba Firdose Fathima ◽  
Monica K. P ◽  
Kusuma Mohanchandra
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Brain Mri ◽  
Memory Loss ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Mri Scans

Alzheimer's disease is an extremely popular cause of dementia which leads to memory loss, problem-solving and other thinking abilities that are severe enough to interfere with daily life. Detection of Alzheimer’s at a prior stage is crucial as it can prevent significant damage to the patient’s brain. In this paper, a method to detect Alzheimer’s  Disease from Brain MRI images is proposed. The proposed approach extracts shape features and texture of the Hippocampus region from the MRI scans and a Neural Network is used as a Multi-Class Classifier for detection of AD. The proposed approach is implemented and it gives better accuracy as compared to conventional approaches. In this paper, Convolutional Neural Network is the Neural Network approach used for the detection of AD at a prodromal stage.

scholarly journals Classification of Sex and Alzheimer’s Disease via Brain Imaging-Based Deep Learning on 85,721 Samples

2020 ◽  
Author(s):  
Bin Lu ◽  
Hui-Xian Li ◽  
Zhi-Kai Chang ◽  
Le Li ◽  
Ning-Xuan Chen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Individual Differences ◽  
Brain Imaging ◽  
Transfer Learning ◽  
Brain Mri ◽  
Classification Model ◽  
Parahippocampal Gyrus ◽  
Industrial Grade

AbstractBeyond detecting brain damage or tumors, little success has been attained on identifying individual differences and brain disorders with magnetic resonance imaging (MRI). Here, we sought to build industrial-grade brain imaging-based classifiers to infer two types of such inter-individual differences: sex and Alzheimer’s disease (AD), using deep learning/transfer learning on big data. We pooled brain structural data from 217 sites/scanners to constitute the largest brain MRI sample to date (85,721 samples from 50,876 participants), and applied a state-of-the-art deep convolutional neural network, Inception-ResNet-V2, to build a sex classifier with high generalizability. In cross-dataset-validation, the sex classification model was able to classify the sex of any participant with brain structural imaging data from any scanner with 94.9% accuracy. We then applied transfer learning based on this model to objectively diagnose AD, achieving 88.4% accuracy in cross-site-validation on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and 91.2% / 86.1% accuracy for a direct test on two unseen independent datasets (AIBL / OASIS). Directly testing this AD classifier on brain images of unseen mild cognitive impairment (MCI) patients, the model correctly predicted 63.2% who eventually converted into AD, versus predicting 22.1% as AD who did not convert into AD during follow-up. Predicted scores of the AD classifier correlated significantly with illness severity. By contrast, the transfer learning framework was unable to achieve practical accuracy for psychiatric disorders. To improve interpretability of the deep learning models, occlusion tests revealed that hypothalamus, superior vermis, thalamus, amygdala and limbic system areas were critical for predicting sex; hippocampus, parahippocampal gyrus, putamen and insula played key roles in predicting AD. Our trained model, code, preprocessed data and an online prediction website have been openly-shared to advance the clinical utility of brain imaging.

scholarly journals A Sports Training Video Classification Model Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yunjun Xu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Camera Calibration ◽  
Classification Accuracy ◽  
Classification Model ◽  
Video Classification ◽  
Sports Training ◽  
Training Video ◽  
Model Based

A sports training video classification model based on deep learning is studied for targeting low classification accuracy caused by the randomness of objective movement in sports training video. The camera calibration technology is used to restore the position of the target in the real three-dimensional space. After the camera calibration in the video, the sports training video is preprocessed. The input video segment is divided into equal length segments to obtain the subvideo segment. The motion vector field, brightness feature, color feature, and texture feature of the subvideo segment are extracted, and the extracted features are input into the AlexNet convolutional neural network. ReLU is used as the activation function in this convolutional neural network. Local response normalization is used to suppress and enhance the output of neurons to highlight the performance of useful information, so that the output classification results are more accurate. Event matching method is used to match the convolutional neural network output to complete the sports training video classification. The experimental results of the proposed study show that the model can effectively solve the problems of target moving randomness. The classification accuracy of sports training video is more than 99%, and the classification speed is faster which is shown from the results of the experiments.

scholarly journals Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Young-Gon Kim ◽  
Sungchul Kim ◽  
Cristina Eunbee Cho ◽  
In Hye Song ◽  
Hee Jin Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Frozen Section ◽  
Medical Center ◽  
External Validation ◽  
Model Performance ◽  
Classification Model ◽  
Training Dataset

AbstractFast and accurate confirmation of metastasis on the frozen tissue section of intraoperative sentinel lymph node biopsy is an essential tool for critical surgical decisions. However, accurate diagnosis by pathologists is difficult within the time limitations. Training a robust and accurate deep learning model is also difficult owing to the limited number of frozen datasets with high quality labels. To overcome these issues, we validated the effectiveness of transfer learning from CAMELYON16 to improve performance of the convolutional neural network (CNN)-based classification model on our frozen dataset (N = 297) from Asan Medical Center (AMC). Among the 297 whole slide images (WSIs), 157 and 40 WSIs were used to train deep learning models with different dataset ratios at 2, 4, 8, 20, 40, and 100%. The remaining, i.e., 100 WSIs, were used to validate model performance in terms of patch- and slide-level classification. An additional 228 WSIs from Seoul National University Bundang Hospital (SNUBH) were used as an external validation. Three initial weights, i.e., scratch-based (random initialization), ImageNet-based, and CAMELYON16-based models were used to validate their effectiveness in external validation. In the patch-level classification results on the AMC dataset, CAMELYON16-based models trained with a small dataset (up to 40%, i.e., 62 WSIs) showed a significantly higher area under the curve (AUC) of 0.929 than those of the scratch- and ImageNet-based models at 0.897 and 0.919, respectively, while CAMELYON16-based and ImageNet-based models trained with 100% of the training dataset showed comparable AUCs at 0.944 and 0.943, respectively. For the external validation, CAMELYON16-based models showed higher AUCs than those of the scratch- and ImageNet-based models. Model performance for slide feasibility of the transfer learning to enhance model performance was validated in the case of frozen section datasets with limited numbers.

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