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Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda
Artificial intelligence can assist providers in a variety of patient care and intelligent health
systems. Artificial intelligence techniques ranging from machine learning to deep learning …
systems. Artificial intelligence techniques ranging from machine learning to deep learning …
Machine learning for dementia prediction: a systematic review and future research directions
Abstract Nowadays, Artificial Intelligence (AI) and machine learning (ML) have successfully
provided automated solutions to numerous real-world problems. Healthcare is one of the …
provided automated solutions to numerous real-world problems. Healthcare is one of the …
[HTML][HTML] IoT and artificial intelligence implementations for remote healthcare monitoring systems: A survey
M Alshamrani - Journal of King Saud University-Computer and …, 2022 - Elsevier
Abstract The Internet of Things (IoT) and artificial intelligence (AI) are two of the fastest-
growing technologies in the world. With more people moving to cities, the concept of a smart …
growing technologies in the world. With more people moving to cities, the concept of a smart …
Machine learning and deep learning approaches for brain disease diagnosis: principles and recent advances
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 …
diseases are being discovered. Thus, because of the variability of brain diseases, existing …
Deep learning-based classification of healthy aging controls, mild cognitive impairment and Alzheimer's disease using fusion of MRI-PET imaging
Automated detection of dementia stage using multimodal imaging modalities will be helpful
for improving the clinical diagnosis. In this study, we develop the Inception-ResNet wrapper …
for improving the clinical diagnosis. In this study, we develop the Inception-ResNet wrapper …
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 …
[HTML][HTML] Constructing domain ontology for Alzheimer disease using deep learning based approach
Facts can be exchanged in multiple fields with the help of disease-specific ontologies. A
range of diverse values can be produced by mining ontological approaches for …
range of diverse values can be produced by mining ontological approaches for …
[HTML][HTML] Development of a three tiered cognitive hybrid machine learning algorithm for effective diagnosis of Alzheimer's disease
Alzheimer's disease (AD) is one of the most frequent neurodegenerative disorders in the
elderly subjects. Since early detection can prevent or delay cognitive decline in the older …
elderly subjects. Since early detection can prevent or delay cognitive decline in the older …
A novel multi-modal depression detection approach based on mobile crowd sensing and task-based mechanisms
Depression has become a global concern, and COVID-19 also has caused a big surge in its
incidence. Broadly, there are two primary methods of detecting depression: Task-based and …
incidence. Broadly, there are two primary methods of detecting depression: Task-based and …
A systematic literature review on the significance of deep learning and machine learning in predicting Alzheimer's disease
Background Alzheimer's disease (AD) is the most prevalent cause of dementia,
characterized by a steady decline in mental, behavioral, and social abilities and impairs a …
characterized by a steady decline in mental, behavioral, and social abilities and impairs a …