Summary of Fever Detection with Infrared Thermography: Enhancing Accuracy Through Machine Learning Techniques, by Parsa Razmara et al.
Fever Detection with Infrared Thermography: Enhancing Accuracy through Machine Learning Techniques
by Parsa Razmara, Tina Khezresmaeilzadeh, B. Keith Jenkins
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study aims to enhance the accuracy and reliability of infrared thermography (IRT) readings, which are crucial for diagnosing febrile conditions like COVID-19. Traditional non-contact infrared thermometers (NCITs) often exhibit significant variability in readings, making it necessary to integrate machine learning algorithms with IRT. The authors systematically evaluated various regression models using heuristic feature engineering techniques, focusing on features’ physiological relevance and statistical significance. The Convolutional Neural Network (CNN) model achieved the lowest root mean squared error (RMSE) of 0.2223, outperforming previous literature. Non-neural network models like Binning also showed promising results with an RMSE of 0.2296. This study highlights 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making infrared thermography (IRT) readings more accurate and reliable. IRT is a way to measure body temperature without touching people, which is important for diagnosing diseases like COVID-19. The authors wanted to make sure the readings were correct, so they used machine learning algorithms with IRT. They tried different methods and found that one type of algorithm called Convolutional Neural Network (CNN) worked really well. This study shows how combining advanced techniques can help create better diagnostic tools for diseases. |
Keywords
» Artificial intelligence » Cnn » Feature engineering » Machine learning » Neural network » Regression » Temperature
