Deep learning based diagnosis and prognosis of Alzheimer's disease: A comprehensive review
Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most
common cause of Dementia. Neuroimaging analyses, such as T1 weighted magnetic …
common cause of Dementia. Neuroimaging analyses, such as T1 weighted magnetic …
Role of artificial intelligence techniques and neuroimaging modalities in detection of Parkinson's disease: a systematic review
Abstract Parkinson's disease (PD), a neurodegenerative disorder, is caused due to the lack
of dopamine neurotransmitters throughout the substantia nigra. Its diagnosis in the earlier …
of dopamine neurotransmitters throughout the substantia nigra. Its diagnosis in the earlier …
Multimodal neuroimaging based Alzheimer's disease diagnosis using evolutionary RVFL classifier
Alzheimer's disease (AD) is one of the most known causes of dementia which can be
characterized by continuous deterioration in the cognitive skills of elderly people. It is a non …
characterized by continuous deterioration in the cognitive skills of elderly people. It is a non …
Speech2eeg: Leveraging pretrained speech model for eeg signal recognition
Identifying meaningful brain activities is critical in brain-computer interface (BCI)
applications. Recently, an increasing number of neural network approaches have been …
applications. Recently, an increasing number of neural network approaches have been …
Dual-stream Representation Fusion Learning for accurate medical image segmentation
Accurate segmenting regions of interest in various medical images are essential to clinical
research and applications. Although deep learning-based methods have achieved good …
research and applications. Although deep learning-based methods have achieved good …
Association of white matter volume with brain age classification using deep learning network and region wise analysis
Structural magnetic resonance imaging (sMRI) has been used to examine age-related
neuroanatomical changes in the human brain. In the present work, a pre-trained deep …
neuroanatomical changes in the human brain. In the present work, a pre-trained deep …
Quantifying Brain and Cognitive Maintenance as Key Indicators for Sustainable Cognitive Aging: Insights from the UK Biobank
Age-related cognitive decline is a global phenomenon that affects individuals worldwide.
The course and extent of this decline are influenced by numerous factors, such as genetics …
The course and extent of this decline are influenced by numerous factors, such as genetics …
Exploring the potential of vgg-16 architecture for accurate brain tumor detection using deep learning
P Gayathri, A Dhavileswarapu, S Ibrahim… - Journal of Computers …, 2023 - jcmm.co.in
This study explores the potential of the VGG-16 architecture, a Convolutional Neural
Network (CNN) model, for accurate brain tumor detection through deep learning. Utilizing a …
Network (CNN) model, for accurate brain tumor detection through deep learning. Utilizing a …
Prediction of brain age using structural magnetic resonance imaging: A comparison of accuracy and test–retest reliability of publicly available software packages
Brain age prediction algorithms using structural magnetic resonance imaging (MRI) aim to
assess the biological age of the human brain. The difference between a person's …
assess the biological age of the human brain. The difference between a person's …
Estimating age and gender from electrocardiogram signals: A comprehensive review of the past decade
Twelve lead electrocardiogram signals capture unique fingerprints about the body's
biological processes and electrical activity of heart muscles. Machine learning and deep …
biological processes and electrical activity of heart muscles. Machine learning and deep …