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Related Experiment Video

Updated: May 30, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Enhanced brain tumor detection and segmentation using densely connected convolutional networks with stacking ensemble

Asadullah Shaikh1, Samina Amin2, Muhammad Ali Zeb2

  • 1Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia; Emerging Technologies Research Lab (ETRL), College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia.

Computers in Biology and Medicine
|January 25, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces SEL-DenseNet201, an advanced stacking ensemble learning model for precise brain tumor (BT) segmentation using MRI scans. The model achieves high accuracy, offering a promising tool for early BT detection and improved patient outcomes.

Keywords:
Brain tumorConvolutional neural networksDenseNet201MRIMobileNet-v3ResNet50SegmentationVGG-19

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Area of Science:

  • Medical imaging analysis
  • Deep learning for healthcare
  • Computational neuroscience

Background:

  • Brain tumors (BT) significantly impact human health, necessitating accurate and early detection for effective treatment.
  • Magnetic Resonance Imaging (MRI) is crucial for BT diagnosis and segmentation, but challenges persist in achieving high accuracy.
  • Deep learning, particularly transfer learning, shows promise in medical image analysis, yet BT segmentation remains complex.

Purpose of the Study:

  • To enhance the accuracy and effectiveness of brain tumor detection and segmentation using MRI.
  • To implement an advanced stacking ensemble learning (SEL) approach for improved BT segmentation performance.
  • To evaluate the efficacy of a novel SEL-DenseNet201 model in augmenting BT segmentation precision.

Main Methods:

  • A stacking ensemble learning (SEL) approach was designed, utilizing a stacked DenseNet201 as the meta-model (SEL-DenseNet201).
  • The SEL-DenseNet201 model was complemented by six diverse base models: MobileNet-v3, 3D-CNN, VGG-16, VGG-19, ResNet50, and AlexNet.
  • The model was trained on brain tumor MRI datasets, incorporating augmentation techniques for enhanced performance and robustness.

Main Results:

  • The proposed SEL-DenseNet201 model achieved a high accuracy of 99.65%.
  • The model demonstrated a significant dice coefficient of 97.43% for brain tumor segmentation.
  • These results indicate superior performance compared to existing brain tumor segmentation methods.

Conclusions:

  • The SEL-DenseNet201 model represents a significant advancement in brain tumor segmentation accuracy.
  • The study highlights the potential of stacking ensemble learning in improving deep learning-based medical image analysis.
  • The developed model shows promise as an initial screening approach for early brain tumor detection with a high success rate.