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A Deep Probabilistic Sensing and Learning Model for Brain Tumor Classification With Fusion-Net and HFCMIK

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|January 30, 2023
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Summary

This study introduces BTFSC-Net, an artificial intelligence tool for brain tumor classification. It achieves high accuracy in segmenting and classifying tumors using advanced image processing and deep learning techniques.

Keywords:
Brain tumor segmentationclassificationdeep learning convolutional neural networkfeature extractionrobust edge analysis

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Accurate brain tumor classification is crucial for effective treatment planning.
  • Existing diagnostic methods can be time-consuming and may lack precision.
  • Developing automated tools can enhance diagnostic efficiency and accuracy.

Purpose of the Study:

  • To implement and evaluate the BTFSC-Net, an AI-based tool for brain tumor classification.
  • To assess the performance of BTFSC-Net in image segmentation and tumor classification.
  • To compare the efficacy of BTFSC-Net against existing methodologies.

Main Methods:

  • Preprocessing of medical images using a hybrid probabilistic wiener filter (HPWF).
  • Fusion of MRI and CT images using a deep learning convolutional neural network (DLCNN) with robust edge analysis (REA).
  • Segmentation of diseased regions via hybrid fuzzy c-means integrated k-means (HFCMIK) clustering.
  • Extraction of hybrid features (texture, color, low-level) using GLCM and RDWT.
  • Classification of tumors as malignant or benign using a deep learning probabilistic neural network (DLPNN).

Main Results:

  • BTFSC-Net achieved 99.21% accuracy in image segmentation.
  • BTFSC-Net demonstrated 99.46% accuracy in tumor classification.
  • The AI tool effectively identified tumorous regions and classified tumor types.

Conclusions:

  • BTFSC-Net significantly outperforms existing methods in brain tumor diagnosis.
  • The developed AI tool shows high potential for clinical application in neuro-oncology.
  • The study highlights the effectiveness of integrated AI and advanced image processing techniques.