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Enhanced MRI-based brain tumor segmentation and feature extraction using Berkeley wavelet transform and ETCCNN.

Dilip Kumar Gokapay1, Sachi Nandan Mohanty1

  • 1School of Computer Science & Engineering (SCOPE), VIT-AP University, Amaravati, Andhra Pradesh, India.

Digital Health
|December 19, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning model for accurate brain tumor detection using MRI images. The efficient two-channel convolutional neural network achieved 98.8% accuracy in identifying brain tumors.

Keywords:
Berkeley's wavelet transformationBrain tumor detectiondeep neural networkmorphological operationregion fillingthresholdingtwo channel convolutional neural network

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Brain tumors are abnormal growths requiring early detection for effective treatment.
  • Magnetic resonance imaging (MRI) is crucial for diagnosing and classifying brain tumors.
  • Timely detection of brain tumors is vital to prevent severe progression or fatality.

Purpose of the Study:

  • To develop an efficient deep learning classifier for accurate brain tumor detection.
  • To classify brain tumors into categories including meningioma, glioma, pituitary, and non-tumor.
  • To improve early detection rates of brain tumors using advanced computational methods.

Main Methods:

  • An efficient two-channel convolutional neural network (CNN) was designed for brain tumor classification.
  • Image augmentation, morphological operations, and Berkeley Wavelet Transform were used for pre-processing and segmentation.
  • The Enhanced Serval Optimization Algorithm optimized the deep neural network's gain parameters in MATLAB.

Main Results:

  • The proposed deep learning model achieved a high detection accuracy of 98.8% for brain tumors.
  • Performance metrics including accuracy, F-measure, kappa, precision, sensitivity, and specificity were evaluated.
  • The model demonstrated superior performance in classifying and detecting brain tumors.

Conclusions:

  • The developed deep learning strategy shows promising results for brain tumor detection.
  • The study highlights the effectiveness of the two-channel CNN approach in medical image analysis.
  • The findings suggest a significant advancement in automated brain tumor diagnostic tools.