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Related Experiment Video

Updated: Jun 13, 2026

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Brain Tumor Classification and Segmentation in MR Images Using EfficientNet and U-Net++ Models.

Reema Alkharaan1, Jana Alobaidi1, Joud Bakarman1

  • 1Department of Information Technology, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia.

Diagnostics (Basel, Switzerland)
|June 12, 2026
PubMed
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This study introduces an integrated deep learning framework for automated brain tumor classification and segmentation using MRI scans, achieving high accuracy in tumor detection and delineation for improved clinical decision support.

Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis
  • Deep Learning for Healthcare

Background:

  • Brain tumor analysis via MRI is complex due to heterogeneity and requires expert interpretation.
  • Existing deep learning models often address classification or segmentation separately, limiting comprehensive analysis.
  • An integrated approach is needed for automated, dual-task brain tumor assessment.

Purpose of the Study:

  • To develop an integrated deep learning framework for simultaneous brain tumor classification and segmentation using MRI.
  • To provide automated diagnostic support by combining tumor-type prediction and boundary delineation.
  • To enhance the efficiency and accuracy of brain tumor analysis workflows.

Main Methods:

  • Utilized EfficientNet-based CNNs for multi-class tumor classification.
Keywords:
brain tumorclassificationdeep learningmagnetic resonance imagingsegmentation

Related Experiment Videos

Last Updated: Jun 13, 2026

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

  • Employed U-Net++ architectures with EfficientNet encoders for tumor segmentation.
  • Conducted experiments on the BRISC2025 dataset (6000 T1-weighted 2D MRI slices) with extensive preprocessing and transfer learning.

    • Main Results:

    • Achieved 99.70% classification accuracy for glioma, meningioma, pituitary tumor, and no-tumor classes.
    • Obtained a Dice score of 0.9055 and IoU of 0.8442 for tumor segmentation.
    • Demonstrated robust detection of small, low-contrast tumors and strong generalization across diverse MRI data.

    Conclusions:

    • The integrated framework shows high performance in both classification and segmentation of brain tumors.
    • The system effectively identifies small and low-contrast tumor regions.
    • This framework shows potential as a reliable decision-support tool for clinical automated brain tumor analysis.