Alzheimer’s Disease Detection from Brain MRI Data using Deep Learning Techniques

2021 ◽  
Author(s):  
DH Chaihtra ◽  
S Vijaya Shetty
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Brain Mri ◽  
Disease Detection ◽  
Learning Techniques

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.

Automatic Diagnosis of Alzheimer’s Disease Using Deep Learning Techniques

2021 ◽  
Vol 19 (11) ◽  
pp. 126-140
Author(s):  
Zahraa S. Aaraji ◽  
Hawraa H. Abbas
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Data Analysis ◽  
Binary Classification ◽  
Brain Mri ◽  
Three Dimensional ◽  
Structural Variations ◽  
Automatic Feature Extraction ◽  
Multi Class Classification

Neuroimaging data analysis has attracted a great deal of attention with respect to the accurate diagnosis of Alzheimer’s disease (AD). Magnetic Resonance Imaging (MRI) scanners have thus been commonly used to study AD-related brain structural variations, providing images that demonstrate both morphometric and anatomical changes in the human brain. Deep learning algorithms have already been effectively exploited in other medical image processing applications to identify features and recognise patterns for many diseases that affect the brain and other organs; this paper extends on this to describe a novel computer aided software pipeline for the classification and early diagnosis of AD. The proposed method uses two types of three-dimensional Convolutional Neural Networks (3D CNN) to facilitate brain MRI data analysis and automatic feature extraction and classification, so that pre-processing and post-processing are utilised to normalise the MRI data and facilitate pattern recognition. The experimental results show that the proposed approach achieves 97.5%, 82.5%, and 83.75% accuracy in terms of binary classification AD vs. cognitively normal (CN), CN vs. mild cognitive impairment (MCI) and MCI vs. AD, respectively, as well as 85% accuracy for multi class-classification, based on publicly available data sets from the Alzheimer’s disease Neuroimaging Initiative (ADNI).

A deep learning based CNN approach on MRI for Alzheimer’s disease detection

2020 ◽  
Vol 13 (4) ◽  
pp. 495-505 ◽  
Author(s):  
Sanjiban Sekhar Roy ◽  
Raghav Sikaria ◽  
Aarti Susan
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Disease Detection

scholarly journals Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN

2022 ◽  
Vol 3 (4) ◽  
pp. 322-335
Author(s):  
C. R. Nagarathna ◽  
M. Kusuma
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Hybrid Model ◽  
Magnetic Resonance Images ◽  
Medical Field ◽  
The Past ◽  
Learning Techniques ◽  
Learning Technique ◽  
Deep Learning Model

Since the past decade, the deep learning techniques are widely used in research. The objective of various applications is achieved using these techniques. The deep learning technique in the medical field helps to find medicines and diagnosis of diseases. The Alzheimer’s is a physical brain disease, on which recently many research are experimented to develop an efficient model that diagnoses the early stages of Alzheimer’s disease. In this paper, a Hybrid model is proposed, which is a combination of VGG19 with additional layers, and a CNN deep learning model for detecting and classifying the different stages of Alzheimer’s and the performance is compared with the CNN model. The Magnetic Resonance Images are used to analyse both models received from the Kaggle dataset. The result shows that the Hybrid model works efficiently in detecting and classifying the different stages of Alzheimer’s.

scholarly journals System for Automatic Assessment of Alzheimer’s Disease Diagnosis Based on Deep Learning Techniques

Proceedings ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 28
Author(s):  
Alejandro Puente-Castro ◽  
Cristian Robert Munteanu ◽  
Enrique Fernandez-Blanco
Keyword(s):  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Reference Data ◽  
Disease Diagnosis ◽  
Resonance Imaging ◽  
Automatic Assessment ◽  
Learning Techniques ◽  
Magnetic Resonance Imaging Mri

Automatic detection of Alzheimer’s disease is a very active area of research. This is due to its usefulness in starting the protocol to stop the inevitable progression of this neurodegenerative disease. This paper proposes a system for the detection of the disease by means of Deep Learning techniques in magnetic resonance imaging (MRI). As a solution, a model of neuronal networks (ANN) and two sets of reference data for training are proposed. Finally, the goodness of this system is verified within the domain of the application.

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.

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