[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124413-en":3,"doc-seo-124413-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124413,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Fever Detection with Infrared Thermography - Enhancing Accuracy through Machine Learning Techniques","The COVID-19 pandemic highlighted the need for advanced diagnostic tools in global health systems. Infrared Thermography (IRT) offers a non-contact approach for measuring body temperature, supporting identification of febrile conditions linked to infectious diseases such as COVID-19. Traditional non-contact infrared thermometers can show substantial reading variability, sometimes with errors up to about 2°C. The study integrates machine learning with IRT, evaluating regression models with heuristic feature engineering based on physiological relevance and statistical significance.","Fever Detection with Infrared Thermography: Enhancing Accuracy through Machine Learning  \nTechniques  \nParsa Razmara* 1 , Tina Khezresmaeilzadeh* 1 , B. Keith Jenkins 1  \narXiv :2407 . 15302v2 [ cs .LG] 10 Aug 2024  \nAbstract—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 noncontact 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.  \nIndex Terms—COVID-19, Infrared Sensors, Temperature Measurement, Infrared Thermography, Machine Learning, Regression analysis, Deep Learning  \nI. INTRODUCTION  \nAS the COVID-19 pandemic continues to challenge global  \nhealth systems, the adoption of advanced diagnostic tools has become crucial. Infrared Thermography (IRT) has emerged as a significant technological advancement, offering a noncontact and efficient method for measuring body temperature [1], [2] . This is vital for identifying elevated body temperatures, a primary indicator of infectious diseases such as COVID-19, which has a prevalence rate of fever in about 78% of confirmed adult cases. Traditional non-contact infrared thermometers (NCITs), however, exhibit significant variability in readings, with error rates as high as 2°C [3],[4] . Consequently, the use of more advanced methods like IRT combined with machine learning algorithms becomes essential to improve the accuracy and reliability of temperature measurements, building on approaches used for COVID-19 prognosis with biomarkers and demographic information [5] . This integration not only  \n*These authors contributed equally to this work. Responsible Authors Contacts: [prazmara@usc.edu](prazmara@usc.edu), [khezresm@usc.edu](khezresm@usc.edu)  \n1University of Southern California (USC), Los Angeles, CA, United States.  \ncontributes to safer and more effective public health screening practices but also aligns with the global need for improved diagnostic tools during pandemics [6], [7] .  \nThe relevance of IRT in medical diagnostics and epidemic prevention is supported by its increasing utilization in various clinical and public settings. Recent studies have highlighted the potential of IRT systems, particularly when calibrated with precise regression techniques, to provide accurate temperature readings essential for detecting potential cases of COVID-19 . For example, [6] demonstrated that calibrated IRT systems, when compared with non-contact infrared thermometers (NCITs), could achieve higher clinical accuracy and repeatability in temperature measurement. Another research [3] focused on using machine learning to predict core body temperatures from IR-measured facial features, revealing that specific regions such as the temple and nose could serve as reliable indicators of body tempe","cbCain8PuF1P0ZIQ","https://ap.wps.com/l/cbCain8PuF1P0ZIQ","pdf",308516,1,9,"English","en",105,"# Introduction\n# Methodology\n## Dataset","[{\"question\":\"Why is fever detection based on infrared thermography important during COVID-19?\",\"answer\":\"IRT enables non-contact body temperature measurement, which supports identifying febrile conditions associated with COVID-19 screening needs.\"},{\"question\":\"What problem with traditional non-contact infrared thermometers motivates this work?\",\"answer\":\"They often produce variable readings, with reported errors up to around 2°C, reducing reliability for diagnostic screening.\"},{\"question\":\"How does the proposed method improve temperature measurement accuracy?\",\"answer\":\"It combines IRT with machine learning, evaluating regression models and applying heuristic feature engineering that emphasizes physiological relevance and statistical significance, with a CNN achieving the lowest RMSE reported in the study.\"}]","Fever Detection with Infrared Thermography - 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