Deep Learning Based Neural Network for Six-Class-Classification of Alzheimer’s Disease Stages Based on MRI Images

2021 ◽  
pp. 15-23
Author(s):  
Tim Rörup ◽  
I. Rojas ◽  
H. Pomares ◽  
P. Glösekötter
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Disease Stages

scholarly journals A Deep Learning Method on Medical Image Dataset Predicting Early Dementia in Patients Alzheimer's Disease using Convolution Neural Network (CNN)

2019 ◽  
Vol 8 (3S) ◽  
pp. 604-609
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Current Trend ◽  
Memory Loss ◽  
Test Accuracy ◽  
Massive Datasets ◽  
Learning Techniques

Memory loss is one of the major dementia where the human has a common loss of memory which shows the person to behave worst and they don’t care them properly. Alzheimer's disease (Ad) is a neurodegenerative disease which affects the brain with mild cognitive impairment.[4] As MCI has several phases where treatment can be consider for avoiding side effects. Deep Learning techniques is the current trend which can handle the images, massive datasets such as unsupervised, supervised and reinforcement progress.[3] A human MRI images is deal with the existing system to find the dementia. In Existing system 82.51% accuracy of classification of neural network was identified [2][3]. Due to several limitations of existing system CNN was proposed. To predict the dementia an algorithm named Logistic regression is used to produce the accuracy more than a loss function. To the test accuracy betterment OASIS project dataset is utilized.

P4-309: MULTIMODAL-CNN: IMPROVED ACCURACY OF MRI-BASED CLASSIFICATION OF ALZHEIMER'S DISEASE BY INCORPORATING CLINICAL DATA IN DEEP LEARNING

2006 ◽  
Vol 14 (7S_Part_30) ◽  
pp. P1574-P1574
Author(s):  
Taeho Jo ◽  
Kwangsik Nho ◽  
Shannon L. Risacher ◽  
Jingwen Yan ◽  
Andrew J. Saykin
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Clinical Data ◽  
Improved Accuracy

P3-014: DEEP LEARNING IN AUTOMATED CLASSIFICATION OF ADVERSE EVENTS IN CLINICAL STUDIES OF ALZHEIMER'S DISEASE

2006 ◽  
Vol 14 (7S_Part_19) ◽  
pp. P1067-P1068
Author(s):  
Pradeep Anand Ravindranath ◽  
Rema Raman ◽  
Tiffany W. Chow ◽  
Michael S. Rafii ◽  
Paul S. Aisen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Adverse Events ◽  
Clinical Studies ◽  
Automated Classification

scholarly journals A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease

2020 ◽  
Vol 10 (2) ◽  
pp. 84 ◽  
Author(s):  
Atif Mehmood ◽  
Muazzam Maqsood ◽  
Muzaffar Bashir ◽  
Yang Shuyuan
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Early Stage ◽  
The State ◽  
Test Accuracy ◽  
Learning Approaches

Alzheimer’s disease (AD) may cause damage to the memory cells permanently, which results in the form of dementia. The diagnosis of Alzheimer’s disease at an early stage is a problematic task for researchers. For this, machine learning and deep convolutional neural network (CNN) based approaches are readily available to solve various problems related to brain image data analysis. In clinical research, magnetic resonance imaging (MRI) is used to diagnose AD. For accurate classification of dementia stages, we need highly discriminative features obtained from MRI images. Recently advanced deep CNN-based models successfully proved their accuracy. However, due to a smaller number of image samples available in the datasets, there exist problems of over-fitting hindering the performance of deep learning approaches. In this research, we developed a Siamese convolutional neural network (SCNN) model inspired by VGG-16 (also called Oxford Net) to classify dementia stages. In our approach, we extend the insufficient and imbalanced data by using augmentation approaches. Experiments are performed on a publicly available dataset open access series of imaging studies (OASIS), by using the proposed approach, an excellent test accuracy of 99.05% is achieved for the classification of dementia stages. We compared our model with the state-of-the-art models and discovered that the proposed model outperformed the state-of-the-art models in terms of performance, efficiency, and accuracy.

Classification of Alzheimer’s Disease via Eight-Layer Convolutional Neural Network with Batch Normalization and Dropout Techniques

2020 ◽  
Vol 10 (5) ◽  
pp. 1040-1048 ◽  
Author(s):  
Xianwei Jiang ◽  
Liang Chang ◽  
Yu-Dong Zhang
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
State Of The Art ◽  
Training Dataset ◽  
Batch Normalization ◽  
Heavy Burden

More than 35 million patients are suffering from Alzheimer’s disease and this number is growing, which puts a heavy burden on countries around the world. Early detection is of benefit, in which the deep learning can aid AD identification effectively and gain ideal results. A novel eight-layer convolutional neural network with batch normalization and dropout techniques for classification of Alzheimer’s disease was proposed. After data augmentation, the training dataset contained 7399 AD patient and 7399 HC subjects. Our eight-layer CNN-BN-DO-DA method yielded a sensitivity of 97.77%, a specificity of 97.76%, a precision of 97.79%, an accuracy of 97.76%, a F1 of 97.76%, and a MCC of 95.56% on the test set, which achieved the best performance in seven state-of-the-art approaches. The results strongly demonstrate that this method can effectively assist the clinical diagnosis of Alzheimer’s disease.

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