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 Experiment Video

Updated: Jan 14, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

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

Published on: April 13, 2013

43.4K

Fine-tuned ResNet34 for efficient brain tumor classification.

Anas Shahin1

  • 1Faculty of Information Technology Engineering, Syrian Virtual University, Damascus, Syria. anas.shahen.2020@gmail.com.

Scientific Reports
|October 22, 2025
PubMed
Summary

This study demonstrates a deep transfer learning approach for brain tumor classification using MRI images. The fine-tuned ResNet-34 model achieved 99.66% accuracy, improving diagnostic precision for glioma, meningioma, and pituitary tumors.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same journal

Effects of audio guided loving kindness meditation on psychological well being and laboratory stress responsiveness in healthy university students.

Scientific reports·2026
Same journal

Adaptive cognitive driven cross modal network for few shot fine grained recognition.

Scientific reports·2026
Same journal

Tegoprazan-based dual therapy versus bismuth-containing quadruple therapy for Helicobacter pylori eradication: a prospective, multicenter, open-label, non-inferiority, randomized controlled trial.

Scientific reports·2026
Same journal

Primary tumor resection prior to peptide receptor radionuclide therapy is associated with improved survival in metastatic gastroenteropancreatic neuroendocrine tumors: a systematic review and meta-analysis.

Scientific reports·2026
Same journal

Sleep duration among medical students and its association with bronchial asthma, anxiety, and depression.

Scientific reports·2026
Same journal

MGMT deficiency augments STING-mediated inflammatory responses accompanied by metabolic alterations in macrophages.

Scientific reports·2026

Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Oncology

Background:

  • Brain tumors significantly reduce life expectancy and necessitate early, accurate diagnosis for effective treatment.
  • Artificial intelligence (AI), especially deep learning and Convolutional Neural Networks (CNNs), offers automated solutions for medical image analysis.

Purpose of the Study:

  • To evaluate the efficacy of deep transfer learning for classifying brain tumors from MRI scans.
  • To develop an automated diagnostic tool for identifying glioma, meningioma, pituitary tumors, and the absence of tumors.

Main Methods:

  • Utilized a 7023-image Brain Tumor MRI Dataset (Figshare, SARTAJ, Br35H) split into training, validation, and testing sets.
  • Employed a fine-tuned ResNet-34 model with a custom classification head.
  • Incorporated data augmentation and the Ranger optimizer for stable model convergence.
Keywords:
Brain tumor classificationDeep convolutional neural networkMRI scansTransfer learning

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

7.6K

Related Experiment Videos

Last Updated: Jan 14, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

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

Published on: April 13, 2013

43.4K
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

7.6K

Main Results:

  • The proposed deep transfer learning model achieved a high accuracy of 99.66% in classifying brain tumors.
  • The performance surpassed existing state-of-the-art methods in brain tumor classification.

Conclusions:

  • Deep transfer learning, specifically with the fine-tuned ResNet-34 model, is highly effective for accurate brain tumor classification.
  • This AI-driven approach holds significant potential for enhancing early diagnosis and patient survival rates.