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A hybrid M-DbneAlexnet for brain tumour detection using MRI images.

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Multi-level brain tumor classification using hybrid coot flamingo search optimization Algorithm Enabled deep learning

Jayasri Kotti1, Manikandan Moovendran2, Mekala Kandasamy3

  • 1Department of Information Technology, GMR Institute of Technology, Rajam, Andhra Pradesh, India.

Network (Bristol, England)
|April 26, 2024
PubMed
Summary

This study introduces a deep learning method for brain tumor (BT) classification using optimized SpinalNet and CootFSOA-LinkNet for accurate segmentation and multi-type tumor identification.

Keywords:
Coot algorithmFlamingo search algorithmLinkNetSpinalNet

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Brain tumor (BT) detection and classification are critical for effective treatment planning.
  • Accurate segmentation of tumor regions from MRI is essential for precise diagnosis.
  • Existing deep learning models require optimization for improved performance in BT analysis.

Purpose of the Study:

  • To propose an innovative multi-level BT classification approach using deep learning.
  • To develop a novel segmentation method for isolating brain tumor areas.
  • To optimize deep learning model structures and hyperparameters for enhanced accuracy.

Main Methods:

  • Utilized Adaptive Kalman Filter (AKF) for MRI image denoising.
  • Employed CootFSOA-LinkNet for tumor segmentation and LinkNet structural optimization.
  • Implemented CootFSOA-SpinalNet for multi-level BT classification and hyperparameter tuning.

Main Results:

  • Achieved superior performance in BT classification with an accuracy of 0.926.
  • Demonstrated high efficacy with a True Positive Rate (TPR) of 0.931 and True Negative Rate (TNR) of 0.925.
  • Successfully classified different brain tumor types including gliomas, pituitary tumors, and meningiomas.

Conclusions:

  • The proposed CootFSOA-SpinalNet deep learning approach significantly improves brain tumor detection and classification accuracy.
  • The novel segmentation technique effectively isolates tumor regions, aiding in precise diagnosis.
  • This method offers a promising advancement in automated brain tumor analysis using medical imaging.