Artificial intelligence techniques for automated diagnosis of neurological disorders
Background: Authors have been advocating the research ideology that a computer-aided
diagnosis (CAD) system trained using lots of patient data and physiological signals and …
diagnosis (CAD) system trained using lots of patient data and physiological signals and …
[HTML][HTML] Applications of artificial intelligence to aid early detection of dementia: a scoping review on current capabilities and future directions
Abstract Background & Objective With populations aging, the number of people with
dementia worldwide is expected to triple to 152 million by 2050. Seventy percent of cases …
dementia worldwide is expected to triple to 152 million by 2050. Seventy percent of cases …
[HTML][HTML] A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer's disease
Alzheimer's disease (AD) is a progressive and irreversible brain degenerative disorder. Mild
cognitive impairment (MCI) is a clinical precursor of AD. Although some treatments can …
cognitive impairment (MCI) is a clinical precursor of AD. Although some treatments can …
Hierarchical fully convolutional network for joint atrophy localization and Alzheimer's disease diagnosis using structural MRI
Structural magnetic resonance imaging (sMRI) has been widely used for computer-aided
diagnosis of neurodegenerative disorders, eg, Alzheimer's disease (AD), due to its …
diagnosis of neurodegenerative disorders, eg, Alzheimer's disease (AD), due to its …
Dual attention multi-instance deep learning for Alzheimer's disease diagnosis with structural MRI
Structural magnetic resonance imaging (sMRI) is widely used for the brain neurological
disease diagnosis, which could reflect the variations of brain. However, due to the local …
disease diagnosis, which could reflect the variations of brain. However, due to the local …
Landmark-based deep multi-instance learning for brain disease diagnosis
Abstract In conventional Magnetic Resonance (MR) image based methods, two stages are
often involved to capture brain structural information for disease diagnosis, ie, 1) manually …
often involved to capture brain structural information for disease diagnosis, ie, 1) manually …
Joint classification and regression via deep multi-task multi-channel learning for Alzheimer's disease diagnosis
In the field of computer-aided Alzheimer's disease (AD) diagnosis, jointly identifying brain
diseases and predicting clinical scores using magnetic resonance imaging (MRI) have …
diseases and predicting clinical scores using magnetic resonance imaging (MRI) have …
Deep convolution neural network based system for early diagnosis of Alzheimer's disease
RR Janghel, YK Rathore - Irbm, 2021 - Elsevier
Abstract Objectives Alzheimer's Disease (AD) is the most general type of dementia. In all
leading countries, it is one of the primary reasons of death in senior citizens. Currently, it is …
leading countries, it is one of the primary reasons of death in senior citizens. Currently, it is …
Tensorizing GAN with high-order pooling for Alzheimer's disease assessment
It is of great significance to apply deep learning for the early diagnosis of Alzheimer's
disease (AD). In this work, a novel tensorizing GAN with high-order pooling is proposed to …
disease (AD). In this work, a novel tensorizing GAN with high-order pooling is proposed to …
A deep convolutional neural network based computer aided diagnosis system for the prediction of Alzheimer's disease in MRI images
In the recent past, biomedical domain has become popular due to digital image processing
of accurate and efficient diagnosis of clinical patients using Computer-Aided Diagnosis …
of accurate and efficient diagnosis of clinical patients using Computer-Aided Diagnosis …