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The important convolution properties include width, area, differentiation, and integration properties.
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Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images.

M Mohammed Thaha1, K Pradeep Mohan Kumar2, B S Murugan3

  • 1Department of Computer Science and Engineering, J.N.N Institute of Engineering, Chennai, India.

Journal of Medical Systems
|July 26, 2019
PubMed
Summary

This study introduces an Enhanced Convolutional Neural Network (ECNN) optimized with a BAT algorithm for automatic brain tumor segmentation in MRIs. This method improves early detection and patient outcomes compared to manual segmentation.

Keywords:
BAT algorithmBrain tumorEarly detectionHybrid CNNPre-processingSegmentationStandardizing intensity scales

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Area of Science:

  • Medical Image Processing
  • Artificial Intelligence in Medicine
  • Neurology

Background:

  • Brain tumor segmentation is crucial for early detection and treatment, significantly impacting patient survival.
  • Manual segmentation is time-consuming and prone to errors, necessitating automated solutions.
  • Existing automated methods may lack precision and efficiency in complex cases.

Purpose of the Study:

  • To develop an optimized automatic brain tumor segmentation method using Enhanced Convolutional Neural Networks (ECNN).
  • To improve the accuracy and efficiency of Magnetic Resonance Imaging (MRI) based tumor segmentation.
  • To leverage BAT algorithm for loss function optimization in deep learning models for medical imaging.

Main Methods:

  • Proposed an Enhanced Convolutional Neural Network (ECNN) architecture with small kernels to mitigate overfitting.
  • Utilized a BAT algorithm for optimizing the loss function of the segmentation model.
  • Implemented pre-processing steps including skull stripping and image enhancement algorithms.
  • Applied the optimized ECNN model for automatic segmentation of brain tumors in MRI scans.

Main Results:

  • The proposed ECNN method demonstrated superior performance in brain tumor segmentation compared to existing techniques.
  • Evaluated performance metrics included precision, recall, and accuracy, showing significant improvements.
  • The optimization strategy effectively enhanced the segmentation accuracy and reliability.

Conclusions:

  • The BAT algorithm-optimized ECNN provides an effective and efficient solution for automatic brain tumor segmentation in MRI.
  • This automated approach holds potential for improving diagnostic speed and accuracy in clinical practice.
  • Future work may explore advanced selection schemes to further enhance segmentation accuracy.