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Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
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Brain tumor detection using statistical and machine learning method.

Javaria Amin1, Muhammad Sharif1, Mudassar Raza1

  • 1Department of Computer Science, COMSATS University Islamabad, Wah Campus, GT Road Wah Cantt, Punjab 47040, Pakistan.

Computer Methods and Programs in Biomedicine
|July 20, 2019
PubMed
Summary
This summary is machine-generated.

Early brain tumor detection using MRI can improve survival rates. This study presents an advanced image processing technique for accurate and early tumor identification, outperforming existing methods.

Keywords:
Fused featuresLBPPF clusteringPixel based resultsWeiner Filter

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Brain tumors are a leading cause of death globally, necessitating early detection for improved patient outcomes.
  • Magnetic Resonance Imaging (MRI) offers clear visualization of tumors, crucial for timely diagnosis and treatment.
  • Early detection of brain tumors significantly increases patient survival rates.

Purpose of the Study:

  • To develop and evaluate an automated method for early brain tumor detection using MRI.
  • To enhance the accuracy and efficiency of tumor segmentation and classification in medical images.
  • To improve patient survival rates through prompt and reliable brain tumor identification.

Main Methods:

  • Utilized Weiner filter with wavelet bands for image denoising and enhancement.
  • Employed Potential Field (PF) clustering for identifying tumor pixel subsets.
  • Applied global thresholding, mathematical morphology, Local Binary Pattern (LBP), and Gabor Wavelet Transform (GWT) for tumor segmentation and feature fusion.

Main Results:

  • Achieved high Peak Signal to Noise Ratio (PSNR), low Mean Squared Error (MSE), and excellent Structured Similarity Index (SSIM) on T2 and Flair MRI sequences.
  • Demonstrated high precision for foreground (FG) and background (BG) pixels, with minimal error regions (ER) on local and BRATS datasets.
  • Attained superior performance in specificity, sensitivity, accuracy, Area Under the Curve (AUC), and Dice Similarity Coefficient (DSC) when fusing LBP and GWT features.

Conclusions:

  • The proposed automated approach significantly enhances early brain tumor detection accuracy.
  • The method demonstrates superior performance compared to existing techniques in tumor segmentation and classification.
  • This advancement holds potential for improving patient prognosis through earlier and more precise diagnosis.