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A hybrid approach for automatic segmentation and classification to detect tuberculosis.

Muzammil Khan1, Abnash Zaman2, Sarwar Shah Khan1

  • 1Department of Computer & Software Technology, University of Swat, KP, Pakistan.

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Summary

This study introduces an enhanced automatic segmentation and classification (AuSC) framework to improve tuberculosis (TB) detection accuracy from X-ray images. The novel approach achieves high diagnostic accuracy across diverse datasets, aiding global health efforts.

Keywords:
AuSC frameworkclassificationhistogram of orientedgradientslocal binary patternssegmentationsupport vector machinetuberculosis detection

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Radiology

Background:

  • Tuberculosis (TB) is a major global infectious disease, with diagnosis challenged by diverse X-ray image datasets.
  • Accurate TB detection from radiological images is crucial, especially in resource-limited settings.

Purpose of the Study:

  • To propose an innovative image processing approach to enhance TB diagnostic accuracy.
  • To integrate this approach within an automatic segmentation and classification (AuSC) framework for healthcare.

Main Methods:

  • The AuSC of detection of TB (AuSC-DTB) framework involves image preprocessing, segmentation (random walker), feature extraction (LBP, HOG), and classification (SVM).
  • The method was validated on four diverse datasets: JSRT, Montgomery, NLM, and Shenzhen.

Main Results:

  • The proposed technique achieved high accuracy rates: 94% (JSRT), 95% (Montgomery), 95% (NLM), and 93% (Shenzhen).
  • Comparative analysis confirmed the superior performance of this hybrid approach over recent studies.

Conclusions:

  • The AuSC framework significantly improves TB diagnostic accuracy using varied X-ray datasets.
  • This methodology shows potential for diagnosing other X-ray detectable diseases and can be adapted for CT and MRI scans.