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Enhanced swin transformer based tuberculosis classification with segmentation using chest X-ray.

P Visu1, V Sathiya2, P Ajitha3

  • 1Department of Artificial Intelligence and Data Science, Velammal Engineering College, Chennai, India.

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|February 20, 2025
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Summary

This study presents a deep learning model for accurate Tuberculosis detection from Chest X-rays. The model achieves high accuracy, improving early disease diagnosis and control.

Keywords:
chest X-rayenhanced lotus effect optimizationenhanced swin transformermulti-layer perceptronresidual pyramid network

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare
  • Deep Learning for Disease Detection

Background:

  • Tuberculosis (TB) poses a significant global health burden, necessitating early detection for effective treatment and control.
  • Chest X-ray (CXR) is a primary diagnostic tool for TB, but traditional interpretation is labor-intensive and error-prone.
  • Deep learning models offer automated, accurate medical image classification, promising improved diagnostic efficiency.

Purpose of the Study:

  • To develop and validate a novel deep learning framework for automated segmentation and classification of Tuberculosis from Chest X-ray images.
  • To enhance the accuracy and reliability of TB diagnosis through advanced image processing and machine learning techniques.
  • To address the limitations of manual interpretation in CXR-based TB detection.

Main Methods:

  • Pre-processing using Adaptive Gaussian Filtering and data augmentation to refine CXR images.
  • Segmentation of relevant regions in CXR using an Attention UNet (A_UNet) architecture.
  • Classification of TB using an Enhanced Swin Transformer (EnSTrans) model integrated with a Residual Pyramid Network-based Multi-layer Perceptron (MLP).
  • Optimization of the EnSTrans model's loss function via the Enhanced Lotus Effect Optimization (EnLeO) Algorithm.

Main Results:

  • The proposed EnSTrans model demonstrated superior performance in Tuberculosis classification.
  • The integrated approach achieved high diagnostic accuracy, recall, precision, F-score, and specificity.
  • The EnLeO algorithm effectively optimized the model's loss function, contributing to enhanced performance.

Conclusions:

  • The developed deep learning model provides a highly accurate and efficient method for Tuberculosis detection using Chest X-rays.
  • This automated approach has the potential to significantly improve early diagnosis, treatment, and public health strategies for Tuberculosis.
  • The study highlights the efficacy of combining advanced segmentation and classification models with novel optimization algorithms in medical image analysis.