Deep-learning-based diagnosis and prognosis of Alzheimer's disease: A comprehensive review

R Sharma, T Goel, M Tanveer, CT Lin… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most
common cause of Dementia. Neuroimaging analyses, such as T1 weighted magnetic …

Estimating age and gender from electrocardiogram signals: A comprehensive review of the past decade

MY Ansari, M Qaraqe, F Charafeddine… - Artificial Intelligence in …, 2023 - Elsevier
Twelve lead electrocardiogram signals capture unique fingerprints about the body's
biological processes and electrical activity of heart muscles. Machine learning and deep …

Multimodal neuroimaging based Alzheimer's disease diagnosis using evolutionary RVFL classifier

T Goel, R Sharma, M Tanveer… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
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 …

Association of white matter volume with brain age classification using deep learning network and region wise analysis

R Pilli, T Goel, R Murugan, M Tanveer - Engineering Applications of …, 2023 - Elsevier
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 …

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 …

Role of artificial intelligence techniques and neuroimaging modalities in detection of Parkinson's disease: a systematic review

N Aggarwal, BS Saini, S Gupta - Cognitive Computation, 2023 - Springer
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 …

Prediction of brain age using structural magnetic resonance imaging: A comparison of accuracy and test–retest reliability of publicly available software packages

RP Dörfel, JM Arenas‐Gomez, PM Fisher… - Human Brain …, 2023 - Wiley Online Library
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 …

BASE: brain age standardized evaluation

L Dular, Ž Špiclin… - NeuroImage, 2024 - Elsevier
Brain age, most commonly inferred from T1-weighted magnetic resonance images (T1w
MRI), is a robust biomarker of brain health and related diseases. Superior accuracy in brain …

Graph embedded intuitionistic fuzzy random vector functional link neural network for class imbalance learning

MA Ganaie, M Sajid, AK Malik… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The domain of machine learning is confronted with a crucial research area known as class
imbalance (CI) learning, which presents considerable hurdles in the precise classification of …

Dual-stream Representation Fusion Learning for accurate medical image segmentation

R Xu, C Wang, S Xu, W Meng, X Zhang - Engineering Applications of …, 2023 - Elsevier
Accurate segmenting regions of interest in various medical images are essential to clinical
research and applications. Although deep learning-based methods have achieved good …