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Automated Brain Tumor Detection using Ideal Shallow Neural Network with Artificial Jellyfish Optimization.

S R Sridhar1, M Akila2, R Asokan3

  • 1Department of Computer Science and Engineering, Muthayammal Engineering College, Namakkal, 637408, India.

Current Medical Imaging
|July 31, 2023
PubMed
Summary

This study introduces an Artificial Jellyfish Optimization-Ideal Shallow Neural Network (AJO-ISNN) model for accurate brain tumor prediction from MRI scans. The AJO-ISNN model achieves high accuracy and efficient segmentation, outperforming existing methods.

Keywords:
Artificial Jellyfish Optimization (AJO)Brain TumorGabor FilteringGrasshopper Optimization Algorithm (GOA) methodologyIdeal Shallow Neural Network (ISNN)Magnetic Resonance Imaging (MRI)Multi-set Feature Extractionand Centroid Weighted Segmentation (CWS)

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare
  • Computational Neuroscience

Background:

  • Automated brain tumor prediction from MRI and CT scans faces challenges with computational complexity and accuracy.
  • Existing automated tools often require significant human intervention for precise tumor area identification.
  • There is a need for advanced computational models to enhance accuracy and reduce complexity in brain tumor analysis.

Purpose of the Study:

  • To develop an improved automated system for brain tumor classification and segmentation using Magnetic Resonance Imaging (MRI).
  • To enhance prediction accuracy and reduce computational complexity in brain tumor analysis.
  • To evaluate the efficacy of an Ideal Shallow Neural Network (ISNN) optimized with an Artificial Jellyfish Optimization (AJO) algorithm.

Main Methods:

  • Utilized MRI images for enhanced informativeness, employing Gabor filtering for noise reduction and histogram equalization for boundary enhancement.
  • Implemented an Ideal Shallow Neural Network (ISNN) optimized by the Artificial Jellyfish Optimization (AJO) algorithm for feature dimensionality reduction and classification.
  • Employed Centroid Weighted Segmentation (WCS) combined with the Grasshopper Optimization Algorithm (GOA) for improved segmentation of brain tumor boundaries.

Main Results:

  • The proposed AJO-ISNN model achieved a classification accuracy of 95.14%, significantly outperforming Convolutional Neural Network (CNN) at 85.41% and VGG 19 at 93.75%.
  • The Centroid Weighted Segmentation with Grasshopper Optimization Algorithm (CWS-GOA) demonstrated a Dice Similarity Coefficient of 93.15% on both BRATS and Kaggle datasets.
  • The model achieved efficient classification and segmentation, processing approximately 200 images in about 65 seconds.

Conclusions:

  • The AJO-ISNN model offers superior accuracy and efficiency for brain tumor classification and segmentation compared to existing methods like multi-cascaded CNN and InceptionV3.
  • The integration of AJO for feature optimization and GOA for segmentation significantly enhances the performance of the neural network model.
  • This research presents a computationally efficient and highly accurate approach for automated brain tumor analysis from medical imaging data.