A review on multimodal medical image fusion: Compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics
Background and objectives Over the past two decades, medical imaging has been
extensively apply to diagnose diseases. Medical experts continue to have difficulties for …
extensively apply to diagnose diseases. Medical experts continue to have difficulties for …
[HTML][HTML] A survey of multimodal information fusion for smart healthcare: Map** the journey from data to wisdom
Multimodal medical data fusion has emerged as a transformative approach in smart
healthcare, enabling a comprehensive understanding of patient health and personalized …
healthcare, enabling a comprehensive understanding of patient health and personalized …
[HTML][HTML] Information fusion and artificial intelligence for smart healthcare: a bibliometric study
With the fast progress in information technologies and artificial intelligence (AI), smart
healthcare has gained considerable momentum. By using advanced technologies like AI …
healthcare has gained considerable momentum. By using advanced technologies like AI …
Morphological feature visualization of Alzheimer's disease via multidirectional perception GAN
The diagnosis of early stages of Alzheimer's disease (AD) is essential for timely treatment to
slow further deterioration. Visualizing the morphological features for early stages of AD is of …
slow further deterioration. Visualizing the morphological features for early stages of AD is of …
A survey on deep learning for neuroimaging-based brain disorder analysis
Deep learning has recently been used for the analysis of neuroimages, such as structural
magnetic resonance imaging (MRI), functional MRI, and positron emission tomography …
magnetic resonance imaging (MRI), functional MRI, and positron emission tomography …
Alzheimer's disease diagnosis from single and multimodal data using machine and deep learning models: Achievements and future directions
Alzheimer's Disease (AD) is the most prevalent and rapidly progressing neurodegenerative
disorder among the elderly and is a leading cause of dementia. AD results in significant …
disorder among the elderly and is a leading cause of dementia. AD results in significant …
A deep feature-based real-time system for Alzheimer disease stage detection
The origin of dementia can be largely attributed to Alzheimer's disease (AD). The
progressive nature of AD causes the brain cell deterioration that eventfully leads to physical …
progressive nature of AD causes the brain cell deterioration that eventfully leads to physical …
CMC: a consensus multi-view clustering model for predicting Alzheimer's disease progression
Abstract Machine learning has been used in the past for the auxiliary diagnosis of
Alzheimer's Disease (AD). However, most existing technologies only explore single-view …
Alzheimer's Disease (AD). However, most existing technologies only explore single-view …
AlzVNet: A volumetric convolutional neural network for multiclass classification of Alzheimer's disease through multiple neuroimaging computational approaches
N Goenka, S Tiwari - Biomedical signal processing and control, 2022 - Elsevier
Alzheimer's disease is a degenerative neurological disease that causes a loss of cognitive
skills and has no known treatment. Alzheimer's disease (AD) must be detected early, before …
skills and has no known treatment. Alzheimer's disease (AD) must be detected early, before …
Convolutional neural network classifies pathological voice change in laryngeal cancer with high accuracy
Voice changes may be the earliest signs in laryngeal cancer. We investigated whether
automated voice signal analysis can be used to distinguish patients with laryngeal cancer …
automated voice signal analysis can be used to distinguish patients with laryngeal cancer …