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An Enhancement of Deep Learning Algorithm for Brain Tumor Segmentation Using Kernel Based CNN with M-SVM.

R Thillaikkarasi1, S Saravanan2

  • 1Department of Electronics and Communication Engineering, Salem College of Engineering and Technology, Salem, India. rthillaikkarasiscet@gmail.com.

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Summary

This study introduces a new deep learning algorithm for accurate brain tumor segmentation in MRI scans. The kernel-based convolutional neural network (CNN) with M-SVM achieves an 84% accuracy, improving early detection and treatment planning for patients.

Keywords:
Brain tumor segmentationDeep learning algorithmImage classificationKernel based CNNM-SVM

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Brain tumors, characterized by abnormal cell growth, pose significant survival risks.
  • Early detection and accurate segmentation of brain tumors are crucial for effective patient treatment.
  • Current MRI-based tumor segmentation faces challenges due to irregular tumor shapes and positions.

Purpose of the Study:

  • To develop a novel, automated deep learning algorithm for precise brain tumor segmentation in MRI images.
  • To improve the accuracy and efficiency of tumor localization for better treatment planning and surgical guidance.

Main Methods:

  • A hybrid approach combining a kernel-based Convolutional Neural Network (CNN) with a Multiple Support Vector Machine (M-SVM).
  • Image preprocessing involved Laplacian of Gaussian (LoG) filtering and Contrast Limited Adaptive Histogram Equalization (CLAHE).
  • Feature extraction focused on tumor shape, position, and surface characteristics for classification using M-SVM.

Main Results:

  • The proposed kernel-based CNN with M-SVM algorithm demonstrated accurate brain tumor segmentation.
  • The technique achieved an evaluation accuracy of approximately 84% when compared to existing methods.
  • The automated segmentation process enhances efficiency and reliability in identifying tumor boundaries.

Conclusions:

  • The novel deep learning algorithm offers a significant advancement in automated brain tumor segmentation from MRI data.
  • Accurate segmentation facilitates precise tumor localization, aiding clinicians in developing optimal treatment strategies.
  • This method shows potential for improving patient outcomes through enhanced diagnostic capabilities.