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Related Experiment Video

Updated: Apr 23, 2026

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

6.4K

LHW-Net: An ensemble-based machine learning framework for brain tumor classification.

Thireesha Suryadevara1, Naveenkumar Mahamkali1, Mudassir Rafi1,2

  • 1Department of Computer Science and Engineering, SRM University AP, Amaravati, Guntur, Andhra Pradesh, India.

Plos One
|April 21, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces LHW-Net, a novel framework for brain tumor classification using combined features (local binary patterns, histogram of oriented gradients, wavelet transform) and machine learning. The approach significantly improves classification accuracy on benchmark datasets.

Area of Science:

  • Medical Imaging
  • Machine Learning
  • Computer Vision

Background:

  • Brain tumor classification remains challenging due to tumor heterogeneity and variable imaging conditions.
  • Accurate classification is crucial for effective treatment planning and patient outcomes.

Purpose of the Study:

  • To develop and validate a novel framework, LHW-Net, for robust brain tumor classification.
  • To enhance classification performance by integrating handcrafted features and ensemble methods.

Main Methods:

  • The LHW-Net framework combines local binary patterns (LBP), histogram of oriented gradients (HOG), and wavelet transform (WT) features.
  • Extracted features are processed using K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Classifier (SVC).
  • A probabilistic score fusion approach is employed to combine individual classifier results for improved performance.

Related Experiment Videos

Last Updated: Apr 23, 2026

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

6.4K

Main Results:

  • The proposed LHW-Net framework demonstrated effectiveness and robustness in brain tumor classification.
  • Experimental results on benchmark datasets show significant improvements in classification performance.
  • The fusion of multiple feature types and classifiers enhances the overall accuracy and reliability.

Conclusions:

  • The LHW-Net framework offers a promising solution for the challenging problem of brain tumor classification.
  • Combining diverse handcrafted features with ensemble machine learning techniques can overcome limitations of individual methods.
  • This approach has the potential to improve diagnostic accuracy in neuro-oncology.