Analysis of brain sub regions using optimization techniques and deep learning method in Alzheimer disease

Chitradevi, D; Sundaravadivel, Prabha

doi:10.1016/j.asoc.2019.105857

Cited by 87 publications

(26 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to clinical evaluation and psychological tests, artificial intelligence (AI)-based computer-aided diagnosis (CAD) methods for staging AD from structured magnetic resonance imaging (sMRI) have been developed [4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Conventional AI techniques require domain expertise and careful engineering for feature extraction [18].…”

Section: Introductionmentioning

confidence: 99%

Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging

et al. 2020

View full text Add to dashboard Cite

Patches from three orthogonal views of selected cerebral regions can be utilized to learn convolutional neural network (CNN) models for staging the Alzheimer disease (AD) spectrum including preclinical AD, mild cognitive impairment due to AD, and dementia due to AD and normal controls. Hippocampi, amygdalae and insulae were selected from the volumetric analysis of structured magnetic resonance images (MRIs). Three-view patches (TVPs) from these regions were fed to the CNN for training. MRIs were classified with the SoftMax-normalized scores of individual model predictions on TVPs. The significance of each region of interest (ROI) for staging the AD spectrum was evaluated and reported. The results of the ensemble classifier are compared with state-of-the-art methods using the same evaluation metrics. Patch-based ROI ensembles provide comparable diagnostic performance for AD staging. In this work, TVP-based ROI analysis using a CNN provides informative landmarks in cerebral MRIs and may have significance in clinical studies and computer-aided diagnosis system design.

show abstract

Section: Introductionmentioning

confidence: 99%

Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The outcome of this paper suggested that it was more difficult to identify patients with MCI than AD. Brain sub regions were exploited in [103], to identify patients with AD. Among the various optimization algorithms reported here for the proper selection of features, it was revealed that Grey Wolf Optimization showed promising results.…”

Section: ) Dl-based Approaches In Ad Diagnosismentioning

confidence: 99%

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

et al. 2021

View full text Add to dashboard Cite

Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of brain diseases at an early stage can make a huge difference in attempting to cure it. In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology. Application of AI in medical science has made brain disease prediction and detection more accurate and precise. In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer's disease (AD), brain tumor, epilepsy, and Parkinson's disease. 147 recent articles on four brain diseases are reviewed considering diverse machine learning and deep learning approaches, modalities, datasets etc. Twenty-two datasets are discussed which are used most frequently in the reviewed articles as a primary source of brain disease data. Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. Finally, key findings from the reviewed articles are summarized and a number of major issues related to machine learning/deep learning-based brain disease diagnostic approaches are discussed. Through this study, we aim at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment. INDEX TERMS Alzheimer's disease, brain tumor, deep learning, epilepsy, Parkinson's disease, machine learning NOMENCLATURE The most commonly used abbreviations in this survey are summarized in Table 1.

show abstract

“…The prediction of Alzheimer disease is being pursued via different approaches such as using MRI data and motion sensor data. For example, Chitradevi et al [10] concentrated on brain subregions analysis for prediction of Alzheimer disease using optimization techniques. The research work carried out concluded that hippocampus biomarker acts as an important biomarker to analyze the Alzheimer disease.…”

Section: Related Workmentioning

confidence: 99%

DeTrAs: deep learning-based healthcare framework for IoT-based assistance of Alzheimer patients

Sharma

Dudeja

Aujla

et al. 2020

Neural Comput & Applic

View full text Add to dashboard Cite

Healthcare 4.0 paradigm aims at realization of data-driven and patient-centric health systems wherein advanced sensors can be deployed to provide personalized assistance. Hence, extreme mentally affected patients from diseases like Alzheimer can be assisted using sophisticated algorithms and enabling technologies. Motivated from this fact, in this paper, DeTrAs: Deep Learning-based Internet of Health Framework for the Assistance of Alzheimer Patients is proposed. DeTrAs works in three phases: (1) A recurrent neural network-based Alzheimer prediction scheme is proposed which uses sensory movement data, (2) an ensemble approach for abnormality tracking for Alzheimer patients is designed which comprises two parts: (a) convolutional neural network-based emotion detection scheme and (b) timestamp window-based natural language processing scheme, and (3) an IoT-based assistance mechanism for the Alzheimer patients is also presented. The evaluation of DeTrAs depicts almost 10–20% improvement in terms of accuracy in contrast to the different existing machine learning algorithms.

show abstract

Analysis of brain sub regions using optimization techniques and deep learning method in Alzheimer disease

Cited by 87 publications

References 48 publications

Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging

Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

DeTrAs: deep learning-based healthcare framework for IoT-based assistance of Alzheimer patients

Contact Info

Product

Resources

About