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Related Experiment Video

Updated: Mar 25, 2026

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

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Brain tumor detection on magnetic resonance imaging scans using the artificial intelligence-based You Only Look Once

Ronghui Zheng1, Shanshan Cai2

  • 1Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, China.

The Journal of International Medical Research
|March 23, 2026
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Summary
This summary is machine-generated.

A new lightweight brain tumor detection model improves accuracy and efficiency for real-time clinical diagnosis by enhancing magnetic resonance imaging analysis. This advanced framework effectively addresses challenges like blurred boundaries in tumor identification.

Keywords:
Attention-based C2f with Frequency-domain Feed-Forward Network (A2C2f-DFFN) moduleC2f with Token Statistics Self-Attention and Dynamic Tanh (C2TSSA-DYT) moduleYou Only Look Once version 12 (YOLOv12n)brain tumor detectiondynamic upsampling operator

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare
  • Computer-Aided Diagnosis

Background:

  • Brain tumor magnetic resonance imaging (MRI) scans present challenges including blurred boundaries and irregular shapes.
  • Automated diagnosis systems require high accuracy and real-time processing capabilities for clinical application.
  • Existing lightweight models may struggle with complex features and boundary definition in medical scans.

Purpose of the Study:

  • To develop a lightweight detection framework for enhanced automated brain tumor diagnosis from MRI scans.
  • To improve the accuracy and robustness of brain tumor detection, addressing issues like blurred boundaries.
  • To meet real-time clinical requirements for efficient computer-aided diagnosis systems.

Main Methods:

  • An improved You Only Look Once version 12 (YOLOv12n) model was developed, incorporating three novel modules.
  • The Attention-based C2f with Frequency-domain Feed-Forward Network (A2C2f-DFFN) module enhances global context and feature reconstruction.
  • The C2f with Token Statistics Self-Attention and Dynamic Tanh (C2TSSA-DYT) module and dynamic upsampling improve robustness and prevent detail loss.

Main Results:

  • The proposed method achieved 93.2% precision, 88.4% recall, and 94.1% mAP@0.5 on the Kaggle brain tumor dataset.
  • Performance surpassed the baseline YOLOv12n and other lightweight models, demonstrating effectiveness in glioma and pituitary tumor cases.
  • The model operates efficiently with 6.0 GFLOPs and 2.76M parameters, enabling real-time inference.

Conclusions:

  • The enhanced YOLOv12n framework offers a strong balance between accuracy and efficiency for brain tumor detection.
  • The model exhibits robustness, making it suitable for clinical computer-aided diagnosis systems.
  • This approach effectively tackles challenges in brain tumor MRI analysis, paving the way for improved diagnostic tools.