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Updated: Jun 26, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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A Computationally Efficient and Improved Brain Tumor Recognition System by MRI-Segmentation Integrated Classification

Namya Musthafa1, Qurban Memon2, Mohammad Masud3

  • 1College of Engineering, United Arab Emirates University, Asharej, Al Ain, Abu Dhabi, UAE.

Journal of Imaging Informatics in Medicine
|June 22, 2026
PubMed
Summary
This summary is machine-generated.

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VISION: View-specific integrated segmentation-classification framework for accurate brain tumor detection in MRI scans.

PloS one·2025
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Predictive performance of machine learning compared to statistical methods in time-to-event analysis of cardiovascular disease: a systematic review protocol.

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This study introduces a unified deep learning model for brain tumor segmentation and classification using MRI scans. The novel framework achieves high accuracy, improving diagnostic support for clinical decision-making.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Brain tumors pose a significant global health challenge, necessitating accurate diagnosis for effective treatment.
  • Current machine learning models often address MRI-based tumor segmentation or classification separately, limiting integrated diagnostic capabilities.

Purpose of the Study:

  • To develop a unified deep learning model capable of performing both brain tumor segmentation and multiclass classification simultaneously.
  • To enhance the accuracy and efficiency of brain tumor diagnosis by integrating segmentation and classification within a single neural network architecture.

Main Methods:

  • A novel deep learning architecture was designed, integrating a segmentation backbone with a classification head.
  • The model was trained and evaluated on three publicly available brain MRI datasets.
Keywords:
Brain tumorMRISegmentationTumor classification

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  • Performance was benchmarked against Convolutional Neural Network (CNN)-based baselines and attention-based models.

    • Main Results:

    • The unified model achieved high classification accuracy (up to 99.6%) and segmentation performance (Dice score up to 0.935) across datasets.
    • Average performance metrics included 96.9% accuracy and 0.966 F1-score on Dataset 1, 99.4% accuracy and 0.984 F1-score on Dataset 2, and 98.2% accuracy and 0.982 F1-score on Dataset 3.
    • The proposed network demonstrated superior performance compared to existing CNN and attention-based models in tumor localization and classification.

    Conclusions:

    • The integrated deep learning framework effectively performs simultaneous brain tumor segmentation and classification on MRI data.
    • The model shows significant promise for improving clinical decision-making in brain tumor diagnosis and treatment planning.
    • The approach offers a computationally efficient and accurate solution for brain tumor analysis.