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DeepTumor: Framework for Brain MR Image Classification, Segmentation and Tumor Detection.

Ghazanfar Latif1,2

  • 1Computer Science Department, Prince Mohammad Bin Fahd University, Khobar 34754, Saudi Arabia.

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Summary

This study introduces DeepTumor, a framework for brain tumor classification and segmentation using Convolutional Neural Networks (CNNs). It accurately classifies tumors into four classes and segments brain MR images, aiding in precise medical interventions.

Keywords:
convolutional neural networksdeep learningglioma tumor classificationneighboring FCMtumor detection frameworktumor segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuroscience

Background:

  • Accurate brain tumor segmentation is critical for patient care and surgical precision.
  • Multi-class tumor classification aids in treatment planning and prognosis.

Purpose of the Study:

  • To develop a framework (DeepTumor) for multistage-multiclass Glioma Tumor classification and segmentation.
  • To improve the accuracy of brain MR image classification and tumor region identification.

Main Methods:

  • Proposed two deep Convolutional Neural Network (CNN) models for binary brain MR image classification (Tumorous/Non-tumorous).
  • Introduced an enhanced Fuzzy C-means (FCM) technique for tumor segmentation.
  • Developed an enhanced CNN model for classifying segmented tumor regions into four Glioma Tumor classes (Edema, Necrosis, Enhancing, Non-enhancing).

Main Results:

  • The proposed CNN models demonstrated effective binary and multiclass tumor classification.
  • The enhanced FCM technique improved tumor segmentation accuracy.
  • Experimental results on the BraTS MRI dataset showed competitive performance against existing models.

Conclusions:

  • The DeepTumor framework offers a robust solution for brain tumor analysis.
  • The proposed methods enhance the precision of brain tumor classification and segmentation.
  • This work contributes to advancing AI applications in neuro-oncology.