Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Classification of Neurotransmitters

Classification of Neurotransmitters

Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...

Neural Circuits

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

Seizures: Classification

Seizures: Classification

Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:

You might also read

Related Articles

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

Sort by

Same author

Junk Food Consumption Trends, Patterns, and Awareness of Its Health Risks Among University Students in Bangladesh: A Cross-Sectional Study.

Health science reports·2026

Same author

The performance evolution of Xue and Yamada-Ota models for local thermal non equilibrium effects on 3D radiative casson trihybrid nanofluid.

Scientific reports·2025

Same author

Investigating the performance of multivariate LSTM models to predict the occurrence of Distributed Denial of Service (DDoS) attack.

PloS one·2025

Same author

Optimized technique for speaker changes detection in multispeaker audio recording using pyknogram and efficient distance metric.

PloS one·2024

Same author

Federated learning inspired Antlion based orchestration for Edge computing environment.

PloS one·2024

Same author

UMobileNetV2 model for semantic segmentation of gastrointestinal tract in MRI scans.

PloS one·2024

Same journal

RETRACTION: Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: CNN Based Multiclass Brain Tumor Detection Using Medical Imaging.

Computational intelligence and neuroscience·2025

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 8, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

A Robust and Novel Approach for Brain Tumor Classification Using Convolutional Neural Network.

Tahia Tazin¹, Sraboni Sarker¹, Punit Gupta²

¹Department of Electrical and Computer Engineering, North South University, Bashundhara, Dhaka 1229, Bangladesh.

Computational Intelligence and Neuroscience

|December 31, 2021

Summary

This summary is machine-generated.

This study uses convolutional neural networks (CNNs) to detect brain tumors from X-ray images, achieving 92% accuracy with MobileNetV2. Early tumor detection improves patient outcomes and treatment planning.

More Related Videos

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Related Experiment Videos

Last Updated: Oct 8, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Brain tumors are aggressive, necessitating early detection for improved patient outcomes.
Medical imaging techniques like CT, MRI, and X-rays are crucial for tumor assessment.
X-ray imaging is explored for brain tumor diagnosis in this study.

Purpose of the Study:

To investigate the use of convolutional neural networks (CNNs) for brain tumor identification from X-ray images.
To enhance the accuracy and reliability of brain tumor diagnosis through deep learning.
To leverage transfer learning strategies for improved model performance.

Main Methods:

Utilized Python and Google Colab for the research.
Employed deep feature extraction using pretrained deep CNN models: VGG19, InceptionV3, and MobileNetV2.
Assessed model performance based on classification accuracy.

Main Results:

MobileNetV2 achieved the highest accuracy at 92%.
InceptionV3 demonstrated 91% accuracy.
VGG19 achieved 88% accuracy.

Conclusions:

Deep learning models, particularly MobileNetV2, show significant potential for accurate brain tumor detection from X-ray images.
Early and reliable tumor identification can prevent severe physical impairments.
This approach aids in timely treatment planning and enhances patient quality of life.