Machine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia
Alzheimer's disease (AD) is one of the most common form of dementia which mostly affects
elderly people. AD identification in early stages is a difficult task in medical practice and …
elderly people. AD identification in early stages is a difficult task in medical practice and …
Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review
Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and
symptoms that appear in early childhood. ASD is also associated with communication …
symptoms that appear in early childhood. ASD is also associated with communication …
Diagnostic of autism spectrum disorder based on structural brain MRI images using, grid search optimization, and convolutional neural networks
In this study, an automatic autism diagnostic model based on sMRI is proposed. This
proposed model consists of two basic stages. The first stage is the preprocessing stage …
proposed model consists of two basic stages. The first stage is the preprocessing stage …
A review of and roadmap for data science and machine learning for the neuropsychiatric phenotype of autism
P Washington, DP Wall - Annual review of biomedical data …, 2023 - annualreviews.org
Autism spectrum disorder (autism) is a neurodevelopmental delay that affects at least 1 in 44
children. Like many neurological disorder phenotypes, the diagnostic features are …
children. Like many neurological disorder phenotypes, the diagnostic features are …
Uncertainty-guided voxel-level supervised contrastive learning for semi-supervised medical image segmentation
Semi-supervised learning reduces overfitting and facilitates medical image segmentation by
regularizing the learning of limited well-annotated data with the knowledge provided by a …
regularizing the learning of limited well-annotated data with the knowledge provided by a …
ASD-SAENet: a sparse autoencoder, and deep-neural network model for detecting autism spectrum disorder (ASD) using fMRI data
F Almuqhim, F Saeed - Frontiers in Computational Neuroscience, 2021 - frontiersin.org
Autism spectrum disorder (ASD) is a heterogenous neurodevelopmental disorder which is
characterized by impaired communication, and limited social interactions. The shortcomings …
characterized by impaired communication, and limited social interactions. The shortcomings …
Edge detection method based on nonlinear spiking neural systems
R Xian, R Lugu, H Peng, Q Yang, X Luo… - International journal of …, 2023 - World Scientific
Nonlinear spiking neural P (NSNP) systems are a class of neural-like computational models
inspired from the nonlinear mechanism of spiking neurons. NSNP systems have a …
inspired from the nonlinear mechanism of spiking neurons. NSNP systems have a …
Detection of autism spectrum disorder (ASD) in children and adults using machine learning
Autism spectrum disorder (ASD) presents a neurological and developmental disorder that
has an impact on the social and cognitive skills of children causing repetitive behaviours …
has an impact on the social and cognitive skills of children causing repetitive behaviours …
An enhanced multi-modal brain graph network for classifying neuropsychiatric disorders
It has been proven that neuropsychiatric disorders (NDs) can be associated with both
structures and functions of brain regions. Thus, data about structures and functions could be …
structures and functions of brain regions. Thus, data about structures and functions could be …
A comparative analysis of meta-heuristic optimization algorithms for feature selection on ML-based classification of heart-related diseases
This study aims to use a machine learning (ML)-based enhanced diagnosis and survival
model to predict heart disease and survival in heart failure by combining the cuckoo search …
model to predict heart disease and survival in heart failure by combining the cuckoo search …