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Related Experiment Video

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An optimal brain tumor detection by convolutional neural network and Enhanced Sparrow Search Algorithm.

Tingting Liu1, Zhi Yuan2, Li Wu1

  • 1Department of Oncology - Cardiology, Affiliated Tumor Hospital, Xinjiang Medical University, Urumqi, Xinjiang, China.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of Engineering in Medicine
|January 13, 2021
PubMed
Summary

This study introduces an optimized deep learning system for automatic brain tumor diagnosis using MRI scans. The novel approach enhances accuracy and reliability in early-stage tumor detection, improving patient care outcomes.

Keywords:
Brain tumorDWTEnhanced Sparrow Search AlgorithmGLCMconvolutional neural network

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Oncology

Background:

  • Accurate brain tumor detection is crucial for effective treatment and patient outcomes.
  • Current diagnostic algorithms struggle with image quality variations, parameter sensitivity, and early-stage detection.

Purpose of the Study:

  • To develop an automated computer-aided system for precise brain tumor diagnosis.
  • To enhance the accuracy and reliability of early-stage brain tumor detection.

Main Methods:

  • Utilized Gray-Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT) for feature extraction from MR images.
  • Employed an optimized Convolutional Neural Network (CNN) for classification, enhanced by a novel Sparrow Search Algorithm (ESSA).
  • Implemented pre-processing, segmentation, feature extraction, and categorization stages.

Main Results:

  • The proposed system demonstrated higher efficiency compared to three state-of-the-art techniques.
  • Achieved reliable diagnosis of tumors, including in early stages of formation.
  • The optimized CNN with ESSA improved diagnostic performance on the Whole Brain Atlas (WBA) database.

Conclusions:

  • The developed computer-aided system offers a more efficient and reliable method for automatic brain tumor diagnosis.
  • The integration of ESSA-optimized CNN significantly advances the capabilities of AI in medical image analysis for oncology.
  • This approach holds promise for improving clinical decision-making and patient management in neuro-oncology.