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

Search research articles

Related Experiment Video

Updated: Jul 29, 2025

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

GAM-SpCaNet: Gradient awareness minimization-based spinal convolution attention network for brain tumor

Chaosheng Tang¹, Bin Li¹, Junding Sun¹

¹School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, PR China.

Journal of King Saud University. Computer and Information Sciences

|May 22, 2023

Summary

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

COX-2 inhibition improves immune system homeostasis and decreases liver damage in septic rats.

The Journal of surgical research·2009

Same author

Mass spectral characterization of organophosphate-labeled, tyrosine-containing peptides: characteristic mass fragments and a new binding motif for organophosphates.

Journal of chromatography. B, Analytical technologies in the biomedical and life sciences·2009

Same author

3D-SURFER: software for high-throughput protein surface comparison and analysis.

Bioinformatics (Oxford, England)·2009

Same author

Total arch replacement with stented elephant trunk technique: a proposed treatment for complicated Stanford type B aortic dissection.

Journal of cardiac surgery·2009

Same author

Top-emitting white organic light-emitting devices with a one-dimensional metallic-dielectric photonic crystal anode.

Optics letters·2009

Same author

[Detection of tick and tick-borne pathogen in some ports of Inner Mongolia].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2009

Same journal

Online label aggregation with incomplete crowd responses.

Journal of King Saud University. Computer and information sciences·2026

Same journal

Attention mechanism and mixup data augmentation for classification of COVID-19 Computed Tomography images.

Journal of King Saud University. Computer and information sciences·2024

Same journal

DeSa COVID-19: Deep salient COVID-19 image-based quality assessment.

Journal of King Saud University. Computer and information sciences·2024

Same journal

iVaccine-Deep: Prediction of COVID-19 mRNA vaccine degradation using deep learning.

Journal of King Saud University. Computer and information sciences·2024

Same journal

Lexical sorting centrality to distinguish spreading abilities of nodes in complex networks under the Susceptible-Infectious-Recovered (SIR) model.

Journal of King Saud University. Computer and information sciences·2024

Same journal

Pruning-based oversampling technique with smoothed bootstrap resampling for imbalanced clinical dataset of Covid-19.

Journal of King Saud University. Computer and information sciences·2024

See all related articles

This summary is machine-generated.

This study introduces SpCaNet, a novel deep learning model for accurate brain tumor classification. SpCaNet achieves 99.28% accuracy, offering a more efficient and lightweight solution for diagnosing central nervous system tumors.

Area of Science:

Neuroscience
Medical Imaging
Artificial Intelligence

Background:

Brain tumors represent a significant health challenge due to high morbidity and mortality.
The complexity of tumor types and imaging manifestations complicates accurate clinical diagnosis and treatment.

Purpose of the Study:

To develop an efficient and lightweight deep learning model for improved brain tumor classification.
To enhance the generalization ability of training algorithms for medical image analysis.

Main Methods:

Construction of SpCaNet (Spinal Convolution Attention Network) incorporating Positional Attention (PA) convolution, Relative self-attention transformer, and Intermittent fully connected (IFC) layers.
Development and application of the Gradient Awareness Minimization (GAM) algorithm for model training, addressing limitations of traditional Stochastic Gradient Descent (SGD).

Keywords:

Brain tumor classification Gradient awareness minimization Intermittent fully connected layer Positional attention convolution block Relative self-attention transformer block

More Related Videos

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: Jul 29, 2025

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

Main Results:

SpCaNet demonstrated superior performance in brain tumor recognition, achieving a highest accuracy of 99.28%.
The proposed SpCaNet model is significantly more lightweight, with over three times fewer parameters compared to state-of-the-art models.
The GAM algorithm outperformed SGD in classification performance, indicating improved generalization.

Conclusions:

SpCaNet offers an effective and efficient deep learning approach for brain tumor classification.
The GAM algorithm enhances model generalization, contributing to better diagnostic accuracy in neuro-oncology.