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Optimal extreme learning machine for diagnosing brain tumor based on modified sailfish optimizer.

Saad Ali Amin1, Mashal Kasem Sulieman Alqudah2, Saleh Ateeq Almutairi3

  • 1College of Engineering and IT, University of Dubai, Academic City, 14143, Dubai, United Arab Emirates.

Heliyon
|January 16, 2025
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Summary
This summary is machine-generated.

This study introduces an automated method for brain tumor detection in MRI scans using a modified Extreme Learning Machine and the Modified Sailfish optimizer. The approach significantly improves diagnostic accuracy and efficiency in medical imaging.

Keywords:
Automated methodBrain tumorComputer-aided detection systemExtreme learning machine (ELM)MRI imagesModified sailfish optimizer

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Computational Neuroscience

Background:

  • Brain tumors require accurate and efficient detection for timely treatment.
  • Magnetic Resonance Imaging (MRI) is a key modality for brain tumor visualization.
  • Existing automated detection methods face challenges in accuracy and efficiency.

Purpose of the Study:

  • To propose a hierarchical automated methodology for brain tumor detection in MRI.
  • To enhance the performance of brain tumor diagnosis using a modified Extreme Learning Machine integrated with the Modified Sailfish optimizer.
  • To improve the accuracy and reduce the diagnosis time for brain tumors.

Main Methods:

  • Image preprocessing techniques to enhance MRI quality and reduce artifacts.
  • Utilizing a modified Extreme Learning Machine (ELM) for tumor classification.
  • Optimizing the ELM using the Modified Sailfish optimizer (MSFO) for improved performance.
  • Validation on the Whole Brain Atlas (WBA) database.

Main Results:

  • The proposed method achieved a high accuracy of 93.95% in brain tumor detection.
  • Outperformed other methods with 100% recall, 91.38% specificity, and 75.64% F1 score.
  • Demonstrated superior efficiency and accuracy compared to End-to-End and CNN methods.

Conclusions:

  • The hierarchical automated methodology shows significant potential for accurate and efficient brain tumor detection in MRI.
  • The integration of ELM with MSFO enhances diagnostic capabilities in medical imaging.
  • This approach can aid healthcare professionals in prompt and precise treatment decisions for brain tumors.