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Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation
Numerous machine learning (ML) approaches have been proposed for automatic
classification of Alzheimer's disease (AD) from brain imaging data. In particular, over 30 …
classification of Alzheimer's disease (AD) from brain imaging data. In particular, over 30 …
Machine learning techniques for the diagnosis of Alzheimer's disease: A review
Alzheimer's disease is an incurable neurodegenerative disease primarily affecting the
elderly population. Efficient automated techniques are needed for early diagnosis of …
elderly population. Efficient automated techniques are needed for early diagnosis of …
A computer-aided diagnosis system for the classification of COVID-19 and non-COVID-19 pneumonia on chest X-ray images by integrating CNN with sparse …
Several infectious diseases have affected the lives of many people and have caused great
dilemmas all over the world. COVID-19 was declared a pandemic caused by a newly …
dilemmas all over the world. COVID-19 was declared a pandemic caused by a newly …
[HTML][HTML] MRI segmentation and classification of human brain using deep learning for diagnosis of Alzheimer's disease: a survey
N Yamanakkanavar, JY Choi, B Lee - Sensors, 2020 - mdpi.com
Many neurological diseases and delineating pathological regions have been analyzed, and
the anatomical structure of the brain researched with the aid of magnetic resonance imaging …
the anatomical structure of the brain researched with the aid of magnetic resonance imaging …
Automated breast cancer diagnosis based on machine learning algorithms
There have been several empirical studies addressing breast cancer using machine
learning and soft computing techniques. Many claim that their algorithms are faster, easier …
learning and soft computing techniques. Many claim that their algorithms are faster, easier …
Towards Alzheimer's disease classification through transfer learning
Detection of Alzheimer's Disease (AD) from neuroimaging data such as MRI through
machine learning have been a subject of intense research in recent years. Recent success …
machine learning have been a subject of intense research in recent years. Recent success …
Alzheimer's disease detection using depthwise separable convolutional neural networks
J Liu, M Li, Y Luo, S Yang, W Li, Y Bi - Computer Methods and Programs in …, 2021 - Elsevier
To diagnose Alzheimer's disease (AD), neuroimaging methods such as magnetic resonance
imaging have been employed. Recent progress in computer vision with deep learning (DL) …
imaging have been employed. Recent progress in computer vision with deep learning (DL) …
Transfer learning with intelligent training data selection for prediction of Alzheimer's disease
Detection of Alzheimer's disease (AD) from neuroimaging data such as MRI through
machine learning has been a subject of intense research in recent years. The recent …
machine learning has been a subject of intense research in recent years. The recent …
Classification of Alzheimer's disease using ensemble of deep neural networks trained through transfer learning
Alzheimer's disease (AD) is one of the deadliest neurodegenerative diseases ailing the
elderly population all over the world. An ensemble of Deep learning (DL) models can learn …
elderly population all over the world. An ensemble of Deep learning (DL) models can learn …
Prediction and classification of Alzheimer's disease based on combined features from apolipoprotein-E genotype, cerebrospinal fluid, MR, and FDG-PET imaging …
Alzheimer's disease (AD), including its mild cognitive impairment (MCI) phase that may or
may not progress into the AD, is the most ordinary form of dementia. It is extremely important …
may not progress into the AD, is the most ordinary form of dementia. It is extremely important …