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September 18, 2025Open Access

Thermal Image based Non Invasive Disease Diagnosis using Nature Inspired Algorithms

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Authors

AKAditya KatariaRTRitu Tiwari

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Overview

This analysis demonstrates improved accuracy in detecting breast cancer and diabetic foot ulcers, suggesting enhanced diagnostic potential.

Key Points

  • The method achieves 98.5% accuracy for breast cancer detection, showcasing significant improvements over traditional methods.
  • Using generative data augmentation, the diagnostic model effectively reduces dataset limitations, enhancing classification accuracy.
  • Automated ROI segmentation enables precise detection of thermal anomalies, crucial for identifying various disease conditions.
  • Integrating nature-inspired algorithms offers a robust framework for adaptable non-invasive medical screening across pathologies.

Cite This Study

Kataria et al. (2025) studied this question.

synapsesocial.com/papers/68d462db31b076d99fa629e7https://doi.org/10.21203/rs.3.rs-7169643/v1
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  4. 4An Automated Thermography-Based Breast Cancer Detection and Localization System2024 · 4 citations
  5. 5Advances in Thermal Imaging: A Convolutional Neural Network Approach for Improved Breast Cancer Diagnosis2024 · 3 citations