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.

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.

scholarly journals Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease

2021 ◽  
Vol 22 (15) ◽  
pp. 7911
Author(s):  
Eugene Lin ◽  
Chieh-Hsin Lin ◽  
Hsien-Yuan Lane
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Future Research ◽  
Learning Approaches ◽  
Learning Models ◽  
Learning Techniques ◽  
Neuroimaging Data ◽  
Similarities And Differences ◽  
Normal Controls

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

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
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