AIMC Topic: Neural Networks, Computer

Clear Filters Showing 19741 to 19750 of 31376 articles

An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm.

Computational and mathematical methods in medicine Jul 14, 2020
Among the currently proposed brain segmentation methods, brain tumor segmentation methods based on traditional image processing and machine learning are not ideal enough. Therefore, deep learning-based brain segmentation methods are widely used. In t...
Image Interpretation, Computer-Assisted Neuroimaging Mathematical Concepts Brain Neoplasms Magnetic Resonance Imaging Deep Learning Models, Neurological Glioma Algorithms Support Vector Machine Databases, Factual Computational Biology Computer Simulation Humans Neural Networks, Computer
View on PubMed DOI

Deep learning for glioblastoma segmentation using preoperative magnetic resonance imaging identifies volumetric features associated with survival.

Acta neurochirurgica Jul 13, 2020
BACKGROUND: Measurement of volumetric features is challenging in glioblastoma. We investigate whether volumetric features derived from preoperative MRI using a convolutional neural network-assisted segmentation is correlated with survival.
Treatment Outcome Magnetic Resonance Imaging Glioblastoma Deep Learning Image Processing, Computer-Assisted Female Aged Male Middle Aged Brain Neoplasms Humans Neural Networks, Computer
View on PubMed DOI

Bidirectional deep recurrent neural networks for process fault classification.

ISA transactions Jul 13, 2020
In this study, a new approach for time series based condition monitoring and fault diagnosis based on bidirectional recurrent neural networks is presented. The application of bidirectional recurrent neural networks essentially provide a viewpoint cha...
Neural Networks, Computer Deep Learning Databases, Genetic
View on PubMed DOI

Investigating object compositionality in Generative Adversarial Networks.

Neural networks : the official journal of the International Neural Network Society Jul 13, 2020
Deep generative models seek to recover the process with which the observed data was generated. They may be used to synthesize new samples or to subsequently extract representations. Successful approaches in the domain of images are driven by several ...
Neural Networks, Computer Image Processing, Computer-Assisted Pattern Recognition, Automated Humans
View on PubMed DOI

Convolutional neural networks and genetic algorithm for visual imagery classification.

Physical and engineering sciences in medicine Jul 13, 2020
Brain-Computer Interface (BCI) systems establish a channel for direct communication between the brain and the outside world without having to use the peripheral nervous system. While most BCI systems use evoked potentials and motor imagery, in the pr...
Female Reproducibility of Results Adult Neural Networks, Computer Algorithms Humans Signal Processing, Computer-Assisted Male Electrodes Electroencephalography Imagery, Psychotherapy
View on PubMed DOI

Deep Reinforcement Learning and Its Neuroscientific Implications.

Neuron Jul 13, 2020
The emergence of powerful artificial intelligence (AI) is defining new research directions in neuroscience. To date, this research has focused largely on deep neural networks trained using supervised learning in tasks such as image classification. Ho...
Neurosciences Models, Psychological Reinforcement, Psychology Neural Networks, Computer Algorithms Models, Neurological Deep Learning Decision Making
View on PubMed DOI

Deep Physiological Model for Blood Glucose Prediction in T1DM Patients.

Sensors (Basel, Switzerland) Jul 13, 2020
Accurate estimations for the near future levels of blood glucose are crucial for Type 1 Diabetes Mellitus (T1DM) patients in order to be able to react on time and avoid hypo and hyper-glycemic episodes. Accurate predictions for blood glucose are the ...
Humans Neural Networks, Computer Models, Biological Machine Learning Algorithms Blood Glucose Insulin Diabetes Mellitus, Type 1
View on PubMed DOI

Deep learning-based BCI for gait decoding from EEG with LSTM recurrent neural network.

Journal of neural engineering Jul 13, 2020
OBJECTIVE: Mobile Brain/Body Imaging (MoBI) frameworks allowed the research community to find evidence of cortical involvement at walking initiation and during locomotion. However, the decoding of gait patterns from brain signals remains an open chal...
Humans Neural Networks, Computer Brain-Computer Interfaces Electroencephalography Gait Deep Learning
View on PubMed DOI

A Monte Carlo based scatter removal method for non-isocentric cone-beam CT acquisitions using a deep convolutional autoencoder.

Physics in medicine and biology Jul 13, 2020
The primary cone-beam computed tomography (CBCT) imaging beam scatters inside the patient and produces a contaminating photon fluence that is registered by the detector. Scattered photons cause artifacts in the image reconstruction, and are partially...
Neural Networks, Computer Cone-Beam Computed Tomography Image Processing, Computer-Assisted Humans Monte Carlo Method Scattering, Radiation Phantoms, Imaging Artifacts Head and Neck Neoplasms Signal-To-Noise Ratio
View on PubMed DOI

Analysis of the pattern recognition algorithm of broadband satellite modulation signal under deformable convolutional neural networks.

PloS one Jul 13, 2020
This research aims to analyze the effects of different parameter estimation on the recognition performance of satellite modulation signals based on deep learning (DL) under low signal to noise ratio (SNR) or channel non-ideal conditions. In this stud...
Signal-To-Noise Ratio Signal Processing, Computer-Assisted Satellite Communications Neural Networks, Computer Pattern Recognition, Automated Deep Learning
View on PubMed DOI
Popular Topics
Recent Journals