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Efficient 3D AlexNet Architecture for Object Recognition Using Syntactic Patterns from Medical Images.

Shilpa Rani1,2, Deepika Ghai3, Sandeep Kumar4

  • 1Department of CSE, Lovely Professional University, Punjab, India.

Computational Intelligence and Neuroscience
|May 31, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel computer-specific pattern recognition method for brain tumor classification in MRI images. The proposed deep neural network model significantly outperforms existing methods in accuracy.

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Area of Science:

  • Computer Vision
  • Medical Image Processing
  • Artificial Intelligence

Background:

  • Object recognition in medical images, particularly brain tumors, is crucial for diagnosis.
  • Human visual recognition is rapid, motivating the development of efficient computer-based methods.
  • Current methods require enhancement for accurate pattern recognition in noisy medical images.

Purpose of the Study:

  • To develop a computer-specific pattern recognition method for identifying objects in medical images, specifically brain tumors.
  • To enhance the accuracy and efficiency of brain tumor classification using advanced algorithms.
  • To improve upon existing models for brain tumor detection and classification.

Main Methods:

  • Utilized an adaptive median filter for noise reduction in MRI images.
  • Applied contrast image enhancement techniques to improve image quality.
  • Employed a cellular logic array processing (CLAP)-based algorithm for wireframe model evaluation and 3D pattern identification.
  • Integrated syntactic pattern recognition for feature vector extraction and 3D AlexNet for brain tumor classification.

Main Results:

  • Successfully identified basic patterns in 3D medical images.
  • Achieved object classification based on pattern frequency.
  • Demonstrated superior performance of the proposed 3D AlexNet model for brain tumor classification.
  • Validated the model using benchmark datasets: Figshare, Brain MRI Kaggle, Medical MRI, and BraTS 2019.

Conclusions:

  • The proposed syntactic pattern recognition and 3D AlexNet model offers a highly effective approach for brain tumor classification.
  • The method significantly enhances accuracy compared to existing models.
  • This work advances computer vision applications in medical image analysis for improved diagnostic tools.