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

You might also read

Related Articles

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

Sort by
Same author

Adaptive geometric-attention network for two-stage lung nodule segmentation and malignancy classification in federated healthcare IoT edge environments.

PloS one·2026
Same author

Development and validation of an immune-related molecular subtyping model based on a four-gene prognostic biomarker signature and multi-omics integrative analysis in Luminal B breast cancer.

Breast cancer research : BCR·2026
Same author

Novel plasmid pCM3 harboring the <i>aph(3)</i> gene confers phosphorylation-driven streptomycin resistance in <i>Clavibacter michiganensis</i>.

mLife·2026
Same author

Double Parasitism by Two Cuckoo Gentes in a Daurian Redstart Nest.

Ecology and evolution·2026
Same author

Fabrication of Dimeric Prodrug Self-Assembly for Efficient Utilization of Aldehyde-Containing Plant Essential Oils in Management of Plant Disease.

ACS applied materials & interfaces·2026
Same author

Affective and Cognitive Distortions-Aided Suicide Risk Prediction for Long-Form Speech in Psychological Support Hotlines.

Bioengineering (Basel, Switzerland)·2026

Related Experiment Video

Updated: Aug 13, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.9K

Computer-Aided Early Melanoma Brain-Tumor Detection Using Deep-Learning Approach.

Rimsha Asad1,2, Saif Ur Rehman2, Azhar Imran3

  • 1School of Software Engineering, Beijing University of Technology, Beijing 100124, China.

Biomedicines
|January 21, 2023
PubMed
Summary

Early brain tumor detection is crucial for patient survival. This study introduces a deep convolutional neural network (CNN) model for accurate automatic brain tumor classification, achieving high accuracy.

Keywords:
brain tumorconvolutional neural networkdeep learningfeature extractionmedical imagery

More Related Videos

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.3K
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

6.9K

Related Experiment Videos

Last Updated: Aug 13, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.9K
Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.3K
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

6.9K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Brain tumors significantly impact neurological function and are a leading cause of mortality.
  • Manual brain tumor detection is challenging due to variations in tumor characteristics, necessitating automated solutions.
  • Early diagnosis is critical to prevent disease progression and improve patient outcomes.

Purpose of the Study:

  • To develop and evaluate an automated system for the early detection and multi-classification of brain tumors.
  • To leverage deep learning techniques for enhanced diagnostic accuracy and efficiency.

Main Methods:

  • Utilized a deep convolutional neural network (CNN) architecture, specifically ResNet-50, for brain tumor classification.
  • Employed the stochastic gradient descent (SGD) optimization algorithm for model training.
  • Evaluated the model's performance on the public Kaggle brain-tumor dataset.

Main Results:

  • The proposed ResNet-50 model achieved a training accuracy of 99.82% and a testing accuracy of 99.5%.
  • The deep learning approach demonstrated superior performance compared to existing baseline methods.
  • The model's high accuracy indicates its potential for reliable brain tumor diagnosis.

Conclusions:

  • The developed deep learning model offers a highly accurate and efficient method for automatic brain tumor detection and classification.
  • The findings suggest the model's applicability beyond brain tumors, potentially aiding in the diagnosis of other diseases.
  • This automated approach can significantly aid clinicians in early diagnosis, improving patient prognosis.