Fever Detection with Infrared Thermography: Enhancing Accuracy through Machine Learning Techniques

TL;DR

This paper demonstrates that integrating machine learning with infrared thermography significantly improves the accuracy of fever detection, surpassing traditional methods and offering promising advancements for non-contact medical diagnostics.

Contribution

The study introduces a systematic evaluation of machine learning models with heuristic feature engineering to enhance infrared thermography-based fever detection accuracy.

Findings

CNN achieved lowest RMSE of 0.2223

Binning method achieved RMSE of 0.2296 among non-neural models

Machine learning improves reliability of non-contact temperature measurements

Abstract

The COVID-19 pandemic has underscored the necessity for advanced diagnostic tools in global health systems. Infrared Thermography (IRT) has proven to be a crucial non-contact method for measuring body temperature, vital for identifying febrile conditions associated with infectious diseases like COVID-19. Traditional non-contact infrared thermometers (NCITs) often exhibit significant variability in readings. To address this, we integrated machine learning algorithms with IRT to enhance the accuracy and reliability of temperature measurements. Our study systematically evaluated various regression models using heuristic feature engineering techniques, focusing on features' physiological relevance and statistical significance. The Convolutional Neural Network (CNN) model, utilizing these techniques, achieved the lowest RMSE of 0.2223, demonstrating superior performance compared to results reported in previous literature. Among non-neural network models, the Binning method achieved the best performance with an RMSE of 0.2296. Our findings highlight the potential of combining advanced feature engineering with machine learning to improve diagnostic tools' effectiveness, with implications extending to other non-contact or remote sensing biomedical applications. This paper offers a comprehensive analysis of these methodologies, providing a foundation for future research in the field of non-invasive medical diagnostics.