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Brain tumor detection using proper orthogonal decomposition integrated with deep learning networks.

Rita Appiah1, Venkatesh Pulletikurthi2, Helber Antonio Esquivel-Puentes3

  • 1School of Nuclear Engineering, Purdue University, West Lafayette, IN 47906, USA.

Computer Methods and Programs in Biomedicine
|April 26, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel approach combining Proper Orthogonal Decomposition (POD) with Convolutional Neural Networks (CNNs) for efficient brain tumor detection using MRI scans. The POD-CNN model achieves high accuracy with reduced computational time, aiding timely diagnosis.

Keywords:
Brain tumor detectionDeep learningExplainable artificial intelligenceProportional orthogonal decompositionTransfer learning

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Area of Science:

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

Background:

  • Brain tumors disrupt normal function, necessitating accurate and timely detection.
  • Machine learning aids diagnosis, but limited data poses training challenges.
  • Low-order models integrated with machine learning offer a viable solution for reliable detection.

Purpose of the Study:

  • To compare the efficacy of Proper Orthogonal Decomposition (POD) coupled with Convolutional Neural Network (CNN) against state-of-the-art models for brain tumor identification in MRI scans.
  • To evaluate the explainability and performance of the POD-CNN model and transfer learning models (MobileNetV2, Inception-v3, ResNet101, VGG-19).
  • To demonstrate the utility of low-model approaches for improved accuracy in brain tumor detection with limited data.

Main Methods:

  • Utilized 2D Magnetic Resonance Imaging (MRI) scans for brain tumor detection.
  • Implemented and compared a Proper Orthogonal Decomposition (POD) and Convolutional Neural Network (CNN) coupled model.
  • Evaluated pre-trained transfer learning models: MobileNetV2, Inception-v3, ResNet101, and VGG-19.
  • Employed Explainable AI techniques, specifically SHAP, for model interpretability.

Main Results:

  • The standard CNN achieved 99.21% accuracy in tumor prediction.
  • The coupled POD-CNN model demonstrated comparable accuracy (95.88%) with approximately one-third of the computational time.
  • Explainable AI (SHAP) indicated MobileNetV2's superior performance in delineating tumor boundaries.

Conclusions:

  • The integration of Proper Orthogonal Decomposition (POD) with Convolutional Neural Networks (CNNs) represents a novel approach for brain tumor detection using minimal MRI data.
  • This study highlights the potential of low-model machine learning approaches to enhance the accuracy and efficiency of tumor detection.
  • The findings support the development of more accessible and computationally efficient AI tools for medical diagnostics.