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Brain MR Image Classification for Glioma Tumor detection using Deep Convolutional Neural Network Features.

Ghazanfar Latif1, D N F Awang Iskandar1, Jaafar Alghazo2

  • 1Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan, Malaysia.

Current Medical Imaging
|March 13, 2020
PubMed
Summary

This study introduces a novel method for brain tumor detection using deep learning and image processing. The approach achieves high accuracy in classifying and segmenting gliomas from MR images.

Keywords:
CNN FeaturesMR image classificationTumor detectionbrain MRIglioma Tumortumor Segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuroscience

Background:

  • Brain tumor detection and classification from MR images is complex due to similar object characteristics.
  • Accurate segmentation and classification are crucial for effective medical treatment planning.

Purpose of the Study:

  • To classify brain MR images as tumorous or non-tumorous with high accuracy using deep features.
  • To improve brain tumor segmentation accuracy.

Main Methods:

  • A four-step process involving image pre-processing, feature extraction via Convolutional Neural Networks (CNN), classification using Multilayer Perceptron, and segmentation with an enhanced Fuzzy C-Means method.
  • Utilized deep features for classification and an enhanced fuzzy C-means algorithm for segmentation.

Main Results:

  • The proposed CNN-based classification achieved an average accuracy of 98.77% on the BRATS-2015 dataset.
  • Demonstrated noticeable improvements in tumor segmentation results.
  • Tested on 40,300 MR images across four modalities, including Low-Grade Glioma (LGG) and High-Grade Glioma (HGG) cases.

Conclusions:

  • The developed method for brain MR image classification and Glioma Tumor detection offers superior performance and high accuracy.
  • The proposed approach is suitable for clinical adoption due to its effectiveness in detecting brain tumors.