ArticleInfrared physics & technology2022
Low-cost thermal imaging with machine learning for non-invasive diagnosis and therapeutic monitoring of pneumonia.
Article in Infrared physics & technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A Two-Stage Cascaded Regression Framework for Automatic Facial Acupoint Localization in Infrared Thermal Images.Sensors (Basel, Switzerland) · 2026Article
- Assessing the Health and Functionality of the Microcirculation Using Thermal Imaging.Journal of biophotonics · 2026Review
- Separating Chickens' Heads and Legs in Thermal Images via Object Detection and Machine Learning Models to Predict Avian Influenza and Newcastle Disease.Animals : an open access journal from MDPI · 2025Article
- Non-Invasive Biomarkers in the Era of Big Data and Machine Learning.Sensors (Basel, Switzerland) · 2025Review
- Mpox in sports: A comprehensive framework for anticipatory planning and risk mitigation in football based on lessons from COVID-19.Biology of sport · 2024Review
- Synchronously in vivo real-time monitoring bacterial load and temperature with evaluating immune response to decipher bacterial infection.Bioengineering & translational medicine · 2024Article
- Photothermal Radiometry Data Analysis by Using Machine Learning.Sensors (Basel, Switzerland) · 2024Article
- A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review.BMC medical imaging · 2024Review
- Heterogeneous fusion of biometric and deep physiological features for accurate porcine cough recognition.PloS one · 2024Article
- Deep learning framework for sensor array precision and accuracy enhancement.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rapid screening and early treatment of lung infection are essential for effective control of many epidemics such as Coronavirus Disease 2019 (COVID-19). Recent studies have demonstrated the potential correlation between lung infection and the change of back skin temperature distribution. Based on these findings, we propose to use low-cost, portable and rapid thermal imaging in combination with image-processing algorithms and machine learning analysis for non-invasive and safe detection of pneumonia. The proposed method was tested in 69 subjects (30 normal adults, 11 cases of fever without pneumonia, 19 cases of general pneumonia and 9 cases of COVID-19) where both RGB and thermal images were acquired from the back of each subject. The acquired images were processed automatically in order to extract multiple location and shape features that distinguish normal subjects from pneumonia patients at a high accuracy of 93
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Registered trials
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