AIMC Topic: Brain

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Neuromorphic behaviour in discontinuous metal films.

Nanoscale horizons
Physical systems that exhibit brain-like behaviour are currently under intense investigation as platforms for neuromorphic computing. We show that discontinuous metal films, comprising irregular flat islands on a substrate and formed using simple eva...

Deep 3D Neural Network for Brain Structures Segmentation Using Self-Attention Modules in MRI Images.

Sensors (Basel, Switzerland)
In recent years, the use of deep learning-based models for developing advanced healthcare systems has been growing due to the results they can achieve. However, the majority of the proposed deep learning-models largely use convolutional and pooling o...

A Novel and Effective Brain Tumor Classification Model Using Deep Feature Fusion and Famous Machine Learning Classifiers.

Computational intelligence and neuroscience
Brain tumors are difficult to treat and cause substantial fatalities worldwide. Medical professionals visually analyze the images and mark out the tumor regions to identify brain tumors, which is time-consuming and prone to error. Researchers have pr...

Age-Related Changes in Functional Connectivity during the Sensorimotor Integration Detected by Artificial Neural Network.

Sensors (Basel, Switzerland)
Large-scale functional connectivity is an important indicator of the brain's normal functioning. The abnormalities in the connectivity pattern can be used as a diagnostic tool to detect various neurological disorders. The present paper describes the ...

Self-supervised Natural Image Reconstruction and Large-scale Semantic Classification from Brain Activity.

NeuroImage
Reconstructing natural images and decoding their semantic category from fMRI brain recordings is challenging. Acquiring sufficient pairs of images and their corresponding fMRI responses, which span the huge space of natural images, is prohibitive. We...

Deep-learning two-photon fiberscopy for video-rate brain imaging in freely-behaving mice.

Nature communications
Scanning two-photon (2P) fiberscopes (also termed endomicroscopes) have the potential to transform our understanding of how discrete neural activity patterns result in distinct behaviors, as they are capable of high resolution, sub cellular imaging y...

Subcortical segmentation of the fetal brain in 3D ultrasound using deep learning.

NeuroImage
The quantification of subcortical volume development from 3D fetal ultrasound can provide important diagnostic information during pregnancy monitoring. However, manual segmentation of subcortical structures in ultrasound volumes is time-consuming and...

Mind the gap: Performance metric evaluation in brain-age prediction.

Human brain mapping
Estimating age based on neuroimaging-derived data has become a popular approach to developing markers for brain integrity and health. While a variety of machine-learning algorithms can provide accurate predictions of age based on brain characteristic...

Brain tumor classification of magnetic resonance images using a novel CNN-based medical image analysis and detection network in comparison with AlexNet.

Journal of population therapeutics and clinical pharmacology = Journal de la therapeutique des populations et de la pharmacologie clinique
AIM: This research work aims at developing an automatic medical image analysis and detection for accurate classification of brain tumors from a magnetic resonance imaging (MRI) dataset. We developed a new MIDNet18 CNN architecture in comparison with ...

Fuzzy C-Means Algorithm-Based ARM-Linux-Embedded System Combined with Magnetic Resonance Imaging for Progression Prediction of Brain Tumors.

Computational and mathematical methods in medicine
The aim of this research was to analyze the application of fuzzy C-means (FCM) algorithm-based ARM-Linux-embedded system in magnetic resonance imaging (MRI) images for prediction of brain tumors. The optimized FCM (OFCM) algorithm was proposed based ...