Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Neural Regulation01:37

Neural Regulation

42.2K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
42.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

In Situ-Prepared Nickel Oxide Electrodes for Electrochemical Detection of Nitrite via Catalytic Reduction Mechanism.

Sensors (Basel, Switzerland)·2026
Same author

Electrochemically induced CoNiOOH Nanosheets enabling nitrite detection through a catalytic reduction mechanism and machine learning-based concentration prediction.

Food chemistry·2026
Same author

Electrochemical sensing device based on Cu/PPy heterostructure: Detection of nitrite at low reduction potentials and machine learning-driven data analysis visualisation.

Journal of hazardous materials·2026
Same author

Occlusion effects, safety, and clinical prognosis of Watchman and LAmbre occluders in left atrial appendage closure.

BMC cardiovascular disorders·2026
Same author

Inhibition of host N-myristoylation compromises the infectivity of SARS-CoV-2 due to Golgi-bypassing egress.

Nature communications·2026
Same author

Stage-Related Alterations in Cortical Functional Connectivity Gradients in Non-Dialysis Patients With Chronic Kidney Disease.

AJNR. American journal of neuroradiology·2026

Related Experiment Video

Updated: Nov 26, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.2K

Detecting pathological brain via ResNet and randomized neural networks.

Siyuan Lu1, Shui-Hua Wang1,2, Yu-Dong Zhang1,2

  • 1School of Informatics, University of Leicester, Leicester, LE1 7RH, UK.

Heliyon
|December 11, 2020
PubMed
Summary

This study introduces a novel computer-aided diagnosis system for detecting pathological brain conditions using ResNet and randomized neural networks. The system offers efficient and comparable performance to existing methods for brain magnetic resonance image analysis.

Keywords:
Chaotic bat algorithmComputer aided diagnosisComputer scienceConvolutional neural networkExtreme learning machineMagnetic resonance imageRandom vector functional linkRandomized neural networksSchmidt neural network

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.0K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.2K

Related Experiment Videos

Last Updated: Nov 26, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.2K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.0K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.2K

Area of Science:

  • Neurology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Brain diseases are a leading cause of mortality.
  • Medical imaging is crucial for brain disease diagnosis.
  • Manual interpretation of medical images is time-consuming.

Purpose of the Study:

  • To develop an automated pathological brain detection system.
  • To enhance the efficiency of brain magnetic resonance image analysis.
  • To compare the performance of different randomized neural networks.

Main Methods:

  • Utilized ResNet as a feature extractor for brain magnetic resonance images.
  • Implemented three randomized neural networks: Schmidt neural network, random vector functional-link net, and extreme learning machine.
  • Trained network weights and biases using the chaotic bat algorithm.

Main Results:

  • The three proposed randomized neural network methods demonstrated similar performance across five runs.
  • The developed system achieved performance comparable to state-of-the-art approaches.
  • The system effectively automates the analysis of medical images for brain disease diagnosis.

Conclusions:

  • The proposed pathological brain detection system shows promise for clinical application.
  • Automated analysis of brain magnetic resonance images can aid physicians in diagnosis.
  • ResNet combined with randomized neural networks offers an effective approach for computer-aided diagnosis.