Low-cost thermal imaging with machine learning for non-invasive diagnosis and therapeutic monitoring of pneumonia

Qu, Yingjie; Meng, Yuquan; Fan, Hongsong; Xu, Ronald X.

doi:10.1016/j.infrared.2022.104201

Cited by 15 publications

(13 citation statements)

References 35 publications

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“…Physiological deep features, or high-level features beyond epistemic knowledge, are derived through deep learning techniques applied to raw thermal images of physiological signals. These features hold immense potential across various applications, including diagnostics and disease prediction [ 36 ]. In our study, we harnessed a lightweight shallow CNN architecture to extract infrared deep physiological features that faithfully mirror real-time surface temperatures of monitored pigs.…”

Section: Methodsmentioning

confidence: 99%

Heterogeneous fusion of biometric and deep physiological features for accurate porcine cough recognition

Wang,

Qi,

et al. 2024

PLoS ONE

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Accurate identification of porcine cough plays a vital role in comprehensive respiratory health monitoring and diagnosis of pigs. It serves as a fundamental prerequisite for stress-free animal health management, reducing pig mortality rates, and improving the economic efficiency of the farming industry. Creating a representative multi-source signal signature for porcine cough is a crucial step toward automating its identification. To this end, a feature fusion method that combines the biological features extracted from the acoustic source segment with the deep physiological features derived from thermal source images is proposed in the paper. First, acoustic features from various domains are extracted from the sound source signals. To determine the most effective combination of sound source features, an SVM-based recursive feature elimination cross-validation algorithm (SVM-RFECV) is employed. Second, a shallow convolutional neural network (named ThermographicNet) is constructed to extract deep physiological features from the thermal source images. Finally, the two heterogeneous features are integrated at an early stage and input into a support vector machine (SVM) for porcine cough recognition. Through rigorous experimentation, the performance of the proposed fusion approach is evaluated, achieving an impressive accuracy of 98.79% in recognizing porcine cough. These results further underscore the effectiveness of combining acoustic source features with heterogeneous deep thermal source features, thereby establishing a robust feature representation for porcine cough recognition.

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Section: Methodsmentioning

confidence: 99%

Heterogeneous fusion of biometric and deep physiological features for accurate porcine cough recognition

Wang,

Qi,

et al. 2024

PLoS ONE

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“…Pneumonia causes lungs inflammation, whilst rapid detection and early cure of lung infections are critical for controlling respiratory epidemics like COVID‐19. Recent research investigations suggest a link amongst back skin temperature and lung infection, delivered 93% precisions while tested on thermally sensing 69 participants (30 normal people, 11 people with fever but no pneumonia, 9 people with COVID‐19, and 19 people with general pneumonia), [ 214 ] devising viability of thermal infrared sensing and imaging a good alternative technique to diagnose pneumonia (Figure 19c) noninvasively and safely. Movable and non‐contact focal dynamic thermal imaging (FDTI) technique has potential of high resolution, label free and high speed in clinical diagnosis for detecting heterogeneity in malignant, benign, and inflammatory tissue.…”

Section: Applicationsmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Luminescence‐Based Infrared Thermal Sensors: Comprehensive Insights

Fahad,

Li,

Zhai

et al. 2023

Small

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Recent chronological breakthroughs in materials innovation, their fabrication, and structural designs for disparate applications have paved transformational ways to subversively digitalize infrared (IR) thermal imaging sensors from traditional to smart. The noninvasive IR thermal imaging sensors are at the cutting edge of developments, exploiting the abilities of nanomaterials to acquire arbitrary, targeted, and tunable responses suitable for integration with host materials and devices, intimately disintegrate variegated signals from the target onto depiction without any discomfort, eliminating motional artifacts and collects precise physiological and physiochemical information in natural contexts. Highlighting several typical examples from recent literature, this review article summarizes an accessible, critical, and authoritative summary of an emerging class of advancement in the modalities of nano and micro‐scale materials and devices, their fabrication designs and applications in infrared thermal sensors. Introduction is begun covering the importance of IR sensors, followed by a survey on sensing capabilities of various nano and micro structural materials, their design architects, and then culminating an overview of their diverse application swaths. The review concludes with a stimulating frontier debate on the opportunities, difficulties, and future approaches in the vibrant sector of infrared thermal imaging sensors.

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“…The visualization is caused by electromagnetic energy emission, referred to as infrared radiation or thermal radiation, with a range of 0.75-1000 µm [25]. IT has the advantage of being fast, non-invasive, cheap [26], and safe based on surface temperature [27]. IT has become the choice in many domains because of these advantages, especially in the fight against COVID-19.…”

Section: Fig 3 Morphology Of Thermal Imagingmentioning

confidence: 99%

Roboswab: A Covid-19 Thermal Imaging Detector Based on Oral and Facial Temperatures

Astawa

Subagia

Vista

et al. 2023

JOIV : Int. J. Inform. Visualization

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The SARS-CoV-2 virus has been the precursor of the coronavirus disease (COVID-19). The symptoms of COVID-19 begin with the common cold and then become very severe, such as those of Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). Currently, polymerase chain reaction (PCR) is used to detect COVID-19 accurately, but it causes some side effects to the patient when the test is performed. Therefore, the proposed "Roboswab" was developed that uses thermal imaging to measure non-contact facial and oral temperature. This study focuses on the performance of the proposed equipment in measuring facial and oral temperature from various distances. Face detection also involves checking whether the subject is wearing a mask or not. Image processing methods with thermal imaging and robotic manipulators are integrated into a contact-free detector that is inexpensive, accurate, and painless. This research has successfully detected masked or non-masked faces and accurately detected facial temperature. The results showed that the accurate measurement of facial temperature with a mask is 90% with an error of +/- 0.05%, while it was 100% without a mask. On the other hand, the oral temperature was measured with 97% accuracy and an error of less than 5%. The optimal distance of the Roboswab to the face for measuring temperature is an average of 60 cm. The Roboswab tool equipped with masked or non-masked face detection can be used for early detection of COVID-19 without direct contact with patients.

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Low-cost thermal imaging with machine learning for non-invasive diagnosis and therapeutic monitoring of pneumonia

Cited by 15 publications

References 35 publications

Heterogeneous fusion of biometric and deep physiological features for accurate porcine cough recognition

Heterogeneous fusion of biometric and deep physiological features for accurate porcine cough recognition

Luminescence‐Based Infrared Thermal Sensors: Comprehensive Insights

Roboswab: A Covid-19 Thermal Imaging Detector Based on Oral and Facial Temperatures

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