Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review

S Grueso, R Viejo-Sobera - Alzheimer's research & therapy, 2021 - Springer
Background An increase in lifespan in our society is a double-edged sword that entails a
growing number of patients with neurocognitive disorders, Alzheimer's disease being the …

Machine learning techniques for the diagnosis of Alzheimer's disease: A review

M Tanveer, B Richhariya, RU Khan… - ACM Transactions on …, 2020 - dl.acm.org
Alzheimer's disease is an incurable neurodegenerative disease primarily affecting the
elderly population. Efficient automated techniques are needed for early diagnosis of …

A survey on U-shaped networks in medical image segmentations

L Liu, J Cheng, Q Quan, FX Wu, YP Wang, J Wang - Neurocomputing, 2020 - Elsevier
The U-shaped network is one of the end-to-end convolutional neural networks (CNNs). In
electron microscope segmentation of ISBI challenge 2012, the concise architecture and …

Classification and prediction of brain disorders using functional connectivity: promising but challenging

Y Du, Z Fu, VD Calhoun - Frontiers in neuroscience, 2018 - frontiersin.org
Brain functional imaging data, especially functional magnetic resonance imaging (fMRI)
data, have been employed to reflect functional integration of the brain. Alteration in brain …

[HTML][HTML] Efficient and low complex architecture for detection and classification of Brain Tumor using RCNN with Two Channel CNN

N Kesav, MG Jibukumar - Journal of King Saud University-Computer and …, 2022 - Elsevier
Abstract The Brain Tumor is one of the most serious scenarios associated with the brain
where a cluster of abnormal cells grows in an uncontrolled fashion. The field of image …

Applications of deep learning to MRI images: A survey

J Liu, Y Pan, M Li, Z Chen, L Tang… - Big Data Mining and …, 2018 - ieeexplore.ieee.org
Deep learning provides exciting solutions in many fields, such as image analysis, natural
language processing, and expert system, and is seen as a key method for various future …

DeepKEGG: a multi-omics data integration framework with biological insights for cancer recurrence prediction and biomarker discovery

W Lan, H Liao, Q Chen, L Zhu, Y Pan… - Briefings in …, 2024 - academic.oup.com
Deep learning-based multi-omics data integration methods have the capability to reveal the
mechanisms of cancer development, discover cancer biomarkers and identify pathogenic …

[HTML][HTML] Ambient assisted living: sco** review of artificial intelligence models, domains, technology, and concerns

M Jovanovic, G Mitrov, E Zdravevski, P Lameski… - Journal of Medical …, 2022 - jmir.org
Background Ambient assisted living (AAL) is a common name for various artificial
intelligence (AI)—infused applications and platforms that support their users in need in …

Classification of autism spectrum disorder by combining brain connectivity and deep neural network classifier

Y Kong, J Gao, Y Xu, Y Pan, J Wang, J Liu - Neurocomputing, 2019 - Elsevier
Autism spectrum disorder (ASD) is a common neurodevelopmental disorder that seriously
affects communication and sociality of patients. It is crucial to accurately identify patients with …

A review on Alzheimer's disease classification from normal controls and mild cognitive impairment using structural MR images

N Garg, MS Choudhry, RM Bodade - Journal of neuroscience methods, 2023 - Elsevier
Alzheimer's disease (AD) is an irreversible neurodegenerative brain disorder that degrades
the memory and cognitive ability in elderly people. The main reason for memory loss and …