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Brain Magnetic Resonance Imaging Classification Using Deep Learning Architectures with Gender and Age.

Imayanmosha Wahlang1, Arnab Kumar Maji1, Goutam Saha1

  • 1Department of Information Technology, North-Eastern Hill University, Shillong 793022, India.

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Summary

This study introduces deep learning for brain tumor classification in MRI scans, incorporating age and gender. The proposed models achieved higher accuracy than traditional methods, improving diagnostic potential.

Keywords:
Convolutional Neural Network (CNN)Deep Neural Network (DNN)Magnetic Resonance Imaging (MRI)Support Vector Machine (SVM)brain tumordeep learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Accurate brain tumor diagnosis relies on effective classification of Magnetic Resonance Imaging (MRI) scans.
  • Previous research primarily utilized Support Vector Machine (SVM) and AlexNet for binary classification of normal versus abnormal brain MRIs.
  • Limitations in existing methods highlight the need for advanced classification techniques.

Purpose of the Study:

  • To develop and evaluate deep learning architectures for classifying brain MRI images into normal or abnormal categories.
  • To investigate the impact of incorporating demographic attributes like age and gender as higher-level features for enhanced classification accuracy.
  • To compare the performance of proposed deep learning models against traditional methods and existing deep learning approaches.

Main Methods:

  • Implementation of deep learning architectures including Convolutional Neural Network (CNN)-based techniques, Deep Neural Network (DNN), LeNet, AlexNet, and ResNet.
  • Integration of age and gender as additional attributes within the classification models.
  • Comparative analysis of proposed models against Support Vector Machine (SVM) and AlexNet using brain MRI datasets.

Main Results:

  • The proposed deep learning models demonstrated superior performance compared to SVM and AlexNet in classifying brain MRI images.
  • A LeNet-inspired model achieved an overall accuracy of 88%, while a CNN-DNN model reached 80%.
  • Best accuracies achieved were 100% (LeNet Inspired Model), 92% (CNN-DNN), 92% (ResNet), and 81% (AlexNet), with SVM at 82%.

Conclusions:

  • Deep learning architectures, particularly when incorporating age and gender attributes, offer significant improvements in brain tumor classification accuracy from MRI.
  • Age and gender are identified as crucial factors that enhance the precision and clinical relevance of brain tumor analysis.
  • The developed deep learning models show promise for more accurate and reliable brain tumor diagnosis, outperforming established methods.