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Multi-Modal Brain Tumor Detection Using Deep Neural Network and Multiclass SVM.

Sarmad Maqsood1, Robertas Damaševičius1, Rytis Maskeliūnas1

  • 1Faculty of Informatics, Kaunas University of Technology, LT-51386 Kaunas, Lithuania.

Medicina (Kaunas, Lithuania)
|August 26, 2022
PubMed
Summary

This study introduces an automated method for brain tumor detection and classification using deep learning and multiclass support vector machines (M-SVM). The approach achieves high accuracy, improving early diagnosis and prognosis for brain cancer patients.

Keywords:
biomedical image processingbrain tumordeep learninglinear contrast stretchingsegmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Brain cancer is a leading global cause of mortality, necessitating improved diagnostic methods.
  • Accurate and early diagnosis of brain tumors via magnetic resonance imaging (MRI) is crucial for patient prognosis.
  • Manual tumor detection is labor-intensive and prone to errors, highlighting the need for automated solutions.

Purpose of the Study:

  • To develop and evaluate an automated method for precise brain tumor detection and classification.
  • To enhance computer-aided diagnosis systems for radiologists in identifying brain tumors.
  • To improve the accuracy and efficiency of brain tumor diagnosis using advanced machine learning techniques.

Main Methods:

  • A five-step methodology involving linear contrast stretching for edge detection.
  • A custom 17-layered deep neural network for brain tumor segmentation.
  • Feature extraction using a modified MobileNetV2 architecture with transfer learning.
  • Entropy-based feature selection coupled with a multiclass support vector machine (M-SVM).
  • Classification of tumor types (meningioma, glioma, pituitary) using M-SVM.

Main Results:

  • The method was validated on the BraTS 2018 and Figshare datasets.
  • Achieved high detection and classification accuracy: 97.47% and 98.92%.
  • Demonstrated superior performance compared to existing methods, both visually and quantitatively.
  • Utilized eXplainable Artificial Intelligence (XAI) for result interpretation.

Conclusions:

  • The proposed automated approach significantly outperforms previous methods for brain tumor detection and classification.
  • The study confirms the effectiveness of the developed technique in enhancing diagnostic accuracy and quantitative evaluation.
  • The findings support the integration of advanced AI methods for improved clinical diagnosis of brain tumors.