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Segmentation and classification of brain tumor using Taylor fire hawk optimization enabled deep learning approach.

Ajit Kumar Rout1, Sumathi D2, Nandakumar S3

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

Electromagnetic Biology and Medicine
|November 8, 2024
PubMed
Summary

This study introduces Taylor Fire Hawk optimization (TFHO) for improved brain tumor segmentation and classification. The novel method enhances diagnostic accuracy, aiding timely and effective patient treatment.

Keywords:
Adaptive median filterM-NetTaylor seriesbrain tumor classificationdenseNetfire hawk optimizer

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Area of Science:

  • Neurology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Brain tumors arise from irregular cell growth, necessitating early diagnosis for effective treatment.
  • Conventional diagnostic models face challenges in timely and accurate brain tumor detection and classification.
  • Accurate segmentation and classification are critical for developing substantial treatment plans.

Purpose of the Study:

  • To implement an optimized deep learning approach for enhanced brain tumor segmentation and classification.
  • To introduce the Taylor Fire Hawk optimization (TFHO) algorithm for improving diagnostic accuracy.
  • To overcome limitations of conventional models in identifying and categorizing brain malignancies.

Main Methods:

  • Adaptive median filter for image de-noising.
  • M-Net model, trained with TFHO, for image segmentation.
  • Image augmentation and feature extraction.
  • DenseNet classifier trained with TFHO for tumor classification.

Main Results:

  • Achieved 94.86% accuracy in brain tumor classification.
  • Demonstrated high diagnostic performance with a True Positive Rate (TPR) of 95.91% and F1-score of 90.98%.
  • Reported a low False Negative Rate (FNR) of 4.37% and Positive Predictive Value (PPV) of 89.33%.

Conclusions:

  • The TFHO-enhanced M-Net and DenseNet model significantly improves brain tumor segmentation and classification accuracy.
  • This optimized approach offers a promising tool for early and precise diagnosis of brain malignancies.
  • The method's high performance metrics suggest potential for clinical application in neuro-oncology.