Diagnosing Alzheimer’s Disease Using Deep Learning Techniques

2021 ◽  
pp. 79-107
Author(s):  
Anamika Mitra ◽  
Supriya Khaitan ◽  
Ali Imam Abidi ◽  
Sudeshna Chakraborty
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
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.

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 Automatic assessment of Alzheimer’s disease diagnosis based on deep learning techniques

2020 ◽  
Vol 120 ◽  
pp. 103764
Author(s):  
Alejandro Puente-Castro ◽  
Enrique Fernandez-Blanco ◽  
Alejandro Pazos ◽  
Cristian R. Munteanu
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Disease Diagnosis ◽  
Automatic Assessment ◽  
Learning Techniques ◽  
Alzheimer’S Disease Diagnosis

scholarly journals Alzheimer’s Disease Diagnosis using Deep Learning Techniques

2020 ◽  
Vol 9 (3) ◽  
pp. 874-880 ◽  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Disease Diagnosis ◽  
Common Disease ◽  
Brain Disorder ◽  
Brain Image ◽  
Learning Techniques ◽  
And Performance

Deep learning is one of the machine learning approach which has shown promising results and performance as compare to traditional algorithms of machine learning in terms of high dimensional data of MRI brain image. In this article the application of deep learning in medical field is addressed. A thorough review of various algorithms of deep learning for diagnosis of Alzheimer’s disease is done, in which this disease is a progressive brain disorder that destroy the brain memory gradually, it is a common disease in older adults which is caused by dementia. It has been obtained in most research papers that the most widely used and represented algorithm is Convolutional Neural Networks (CNN) when it deals with brain image analysis. After study of various related papers for diagnosing of AD, we have come to this point and suggested that the AD prediction at earlier stages can be increased by using an advance deep learning techniques in different dataset (ADNI, OASIS) combining to one.

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 Deep Learning techniques for effective diagnosis of Alzheimer's disease using MRI images

2021 ◽  
Vol 40 ◽  
pp. 03021
Author(s):  
Prajakta Tambe ◽  
Rutuja Saigaonkar ◽  
Nidhi Devadiga ◽  
Pallavi H. Chitte
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Learning Strategies ◽  
Current Phase ◽  
Convolutional Network ◽  
Great Achievement ◽  
Learning Techniques ◽  
Neuroimaging Data ◽  
Effective Diagnosis

The determination of Alzheimer’s disease (AD) from neuroimaging data such as MRI has been immensely researched over the last few years. Recent advances in deep learning from a computer perspective have advanced in that research. However, the general limitations of such algorithms depend on the large number of training images, as well as the need to carefully perform the construction of deep networks. In past few days deep learning strategies have found great achievement in the analysis of medical imaging. But very little has been done in the use of deep learning strategies to turn up and differentiate Alzheimer’s disease. We are building a deep convolutional network and demonstrating performance on the ADNI-Alzheimer’s Disease Neuroimaging Initiative Dataset. We present a deep convolutional neural network to recognize Alzheimer and differentiate according the current phase of the disease.

scholarly journals Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Manan Binth Taj Noor ◽  
Nusrat Zerin Zenia ◽  
M Shamim Kaiser ◽  
Shamim Al Mamun ◽  
Mufti Mahmud
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Neurological Disorders ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Comparative Performance

Abstract Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

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