AIMC Topic: Brain

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Brain Magnetic Resonance Imaging Classification Using Deep Learning Architectures with Gender and Age.

Sensors (Basel, Switzerland)
Usage of effective classification techniques on Magnetic Resonance Imaging (MRI) helps in the proper diagnosis of brain tumors. Previous studies have focused on the classification of normal (nontumorous) or abnormal (tumorous) brain MRIs using method...

Enhanced Watershed Segmentation Algorithm-Based Modified ResNet50 Model for Brain Tumor Detection.

BioMed research international
This work delivers a novel technique to detect brain tumor with the help of enhanced watershed modeling integrated with a modified ResNet50 architecture. It also involves stochastic approaches to help in developing enhanced watershed modeling. Cancer...

Reproducible neuroimaging features for diagnosis of autism spectrum disorder with machine learning.

Scientific reports
Autism spectrum disorder (ASD) is the fourth most common neurodevelopmental disorder, with a prevalence of 1 in 160 children. Accurate diagnosis relies on experts, but such individuals are scarce. This has led to increasing interest in the developmen...

The Effects of Rso2 and PI Monitoring Images on the Treatment of Premature Infants Based on Deep Learning.

Computational and mathematical methods in medicine
In recent years, due to the combined effects of individual behavior, psychological factors, environmental exposure, medical conditions, biological factors, etc., the incidence of preterm birth has gradually increased, so the incidence of various comp...

Spatiotemporal dynamics in spiking recurrent neural networks using modified-full-FORCE on EEG signals.

Scientific reports
Methods on modelling the human brain as a Complex System have increased remarkably in the literature as researchers seek to understand the underlying foundations behind cognition, behaviour, and perception. Computational methods, especially Graph The...

Characterizing Brain Signals for Epileptic Pre-ictal Signal Classification.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Epilepsy is a kind of neurological disorder characterized by recurrent epileptic seizures. While it is crucial to characterize pre-ictal brain electrical activities, the problem to this day still remains computationally challenging. Using brain signa...

Deep learning identifies brain structures that predict cognition and explain heterogeneity in cognitive aging.

NeuroImage
Specific brain structures (gray matter regions and white matter tracts) play a dominant role in determining cognitive decline and explain the heterogeneity in cognitive aging. Identification of these structures is crucial for screening of older adult...

Progress and Challenges for Memtransistors in Neuromorphic Circuits and Systems.

Advanced materials (Deerfield Beach, Fla.)
Due to the increasing importance of artificial intelligence (AI), significant recent effort has been devoted to the development of neuromorphic circuits that seek to emulate the energy-efficient information processing of the brain. While non-volatile...

Accelerating susceptibility-weighted imaging with deep learning by complex-valued convolutional neural network (ComplexNet): validation in clinical brain imaging.

European radiology
OBJECTIVES: Susceptibility-weighted imaging (SWI) is crucial for the characterization of intracranial hemorrhage and mineralization, but has the drawback of long acquisition times. We aimed to propose a deep learning model to accelerate SWI, and eval...

An EEG Classification-Based Method for Single-Trial N170 Latency Detection and Estimation.

Computational and mathematical methods in medicine
Event-related potentials (ERPs) can reflect the high-level thinking activities of the brain. In ERP analysis, the superposition and averaging method is often used to estimate ERPs. However, the single-trial ERP estimation can provide researchers with...