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Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning

Muhammad Attique Khan1, Imran Ashraf2, Majed Alhaisoni3

  • 1Department of Computer Science, HITEC University, Museum Road, Taxila 47080, Pakistan.

Diagnostics (Basel, Switzerland)
|August 13, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an automated deep learning system for classifying multimodal brain tumors, achieving high accuracy. The advanced method aids radiologists by automating complex tumor type identification.

Keywords:
ELMPLSbrain tumordeep learning featuresfeature fusionfeature selectionhealthcarelinear contrasttransfer learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Manual brain tumor identification is error-prone and time-consuming for radiologists.
  • Multimodal brain tumor classification (T1, T2, T1CE, Flair) presents a significant challenge.
  • Automated systems are crucial for improving diagnostic accuracy and efficiency.

Purpose of the Study:

  • To develop and evaluate an automated deep learning method for multimodal brain tumor classification.
  • To enhance the accuracy and efficiency of brain tumor diagnosis.

Main Methods:

  • Utilized deep learning with pre-trained VGG16 and VGG19 convolutional neural network (CNN) models for feature extraction.
  • Implemented a correntropy-based joint learning approach with extreme learning machine (ELM) for feature selection.
  • Employed partial least square (PLS) for feature fusion, followed by ELM for final classification.

Main Results:

  • Achieved high classification accuracies on the BraTS datasets: 97.8% (BraTS2015), 96.9% (BraTS2017), and 92.5% (BraTS2018).
  • Demonstrated the effectiveness of the proposed automated multimodal classification method.
  • Validated the system's performance on challenging, real-world brain tumor datasets.

Conclusions:

  • The proposed deep learning approach offers a robust and accurate solution for automated multimodal brain tumor classification.
  • This automated system can significantly assist radiologists in diagnosing brain tumors more efficiently and accurately.
  • The method shows promise for clinical application in neuro-oncology.