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Enhancing Medical Image Segmentation and Classification Using a Fuzzy-Driven Method.

Akmal Abduvaitov1, Abror Shavkatovich Buriboev2,3,4, Djamshid Sultanov2

  • 1Department of Information Technologies, Samarkand Branch of Tashkent University of Information Technologies, Samarkand 140100, Uzbekistan.

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Summary
This summary is machine-generated.

This study introduces a 12-step fuzzy-based image enhancement pipeline to improve medical image quality for automated tumor segmentation and disease classification. The method significantly boosts accuracy in CT, MRI, and X-ray modalities.

Keywords:
concatenated CNNfuzzy approachimage enhancement

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Medical image analysis for tumor segmentation and disease classification faces challenges due to noise, low contrast, and ambiguity.
  • Existing enhancement techniques may not adequately address these limitations across various imaging modalities.

Purpose of the Study:

  • To develop and evaluate a novel 12-step fuzzy-based image enhancement pipeline for improving medical image quality.
  • To assess the pipeline's effectiveness in enhancing segmentation and classification accuracy for tumors and diseases in CT, MRI, and X-ray datasets.

Main Methods:

  • A 12-step fuzzy-based enhancement pipeline utilizing fuzzy entropy, fuzzy standard deviation, and histogram spread functions was developed.
  • The pipeline was applied to CT (KiTS19), MRI (BraTS2020), and X-ray (Chest X-ray Pneumonia) datasets.
  • Improved datasets were used to train a Concatenated CNN (CCNN) for segmentation and classification tasks, with comparisons to baseline and CLAHE methods.

Main Results:

  • The pipeline significantly reduced BRISQUE scores across datasets, indicating improved image quality (e.g., KiTS19: 28.8 to 21.7).
  • The CCNN achieved high performance: 99.60% Dice coefficient for kidney tumor segmentation (KiTS19) and high segmentation/classification accuracies for brain tumors (BraTS2020) and pneumonia (Chest X-ray).
  • Performance metrics demonstrated superiority over baseline and CLAHE-enhanced methods.

Conclusions:

  • The proposed fuzzy-based enhancement pipeline effectively improves medical image quality across CT, MRI, and X-ray modalities.
  • Enhanced image quality leads to significantly improved automated tumor segmentation and disease classification accuracy.
  • The pipeline offers a scalable and interpretable foundation for advanced clinical diagnostics.