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Xiao P, Chen D. Photothermal Radiometry Data Analysis by Using Machine Learning. SENSORS (BASEL, SWITZERLAND) 2024; 24:3015. [PMID: 38793869 PMCID: PMC11124847 DOI: 10.3390/s24103015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 04/16/2024] [Accepted: 05/06/2024] [Indexed: 05/26/2024]
Abstract
Photothermal techniques are infrared remote sensing techniques that have been used for biomedical applications, as well as industrial non-destructive testing (NDT). Machine learning is a branch of artificial intelligence, which includes a set of algorithms for learning from past data and analyzing new data, without being explicitly programmed to do so. In this paper, we first review the latest development of machine learning and its applications in photothermal techniques. Next, we present our latest work on machine learning for data analysis in opto-thermal transient emission radiometry (OTTER), which is a type of photothermal technique that has been extensively used in skin hydration, skin hydration depth profiles, skin pigments, as well as topically applied substances and skin penetration measurements. We have investigated different algorithms, such as random forest regression, gradient boosting regression, support vector machine (SVM) regression, and partial least squares regression, as well as deep learning neural network regression. We first introduce the theoretical background, then illustrate its applications with experimental results.
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Affiliation(s)
- Perry Xiao
- School of Engineering, London South Bank University, London SE1 0AA, UK;
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Kumar S, Kumar H, Kumar G, Singh SP, Bijalwan A, Diwakar M. A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review. BMC Med Imaging 2024; 24:30. [PMID: 38302883 PMCID: PMC10832080 DOI: 10.1186/s12880-024-01192-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 01/03/2024] [Indexed: 02/03/2024] Open
Abstract
BACKGROUND Lung diseases, both infectious and non-infectious, are the most prevalent cause of mortality overall in the world. Medical research has identified pneumonia, lung cancer, and Corona Virus Disease 2019 (COVID-19) as prominent lung diseases prioritized over others. Imaging modalities, including X-rays, computer tomography (CT) scans, magnetic resonance imaging (MRIs), positron emission tomography (PET) scans, and others, are primarily employed in medical assessments because they provide computed data that can be utilized as input datasets for computer-assisted diagnostic systems. Imaging datasets are used to develop and evaluate machine learning (ML) methods to analyze and predict prominent lung diseases. OBJECTIVE This review analyzes ML paradigms, imaging modalities' utilization, and recent developments for prominent lung diseases. Furthermore, the research also explores various datasets available publically that are being used for prominent lung diseases. METHODS The well-known databases of academic studies that have been subjected to peer review, namely ScienceDirect, arXiv, IEEE Xplore, MDPI, and many more, were used for the search of relevant articles. Applied keywords and combinations used to search procedures with primary considerations for review, such as pneumonia, lung cancer, COVID-19, various imaging modalities, ML, convolutional neural networks (CNNs), transfer learning, and ensemble learning. RESULTS This research finding indicates that X-ray datasets are preferred for detecting pneumonia, while CT scan datasets are predominantly favored for detecting lung cancer. Furthermore, in COVID-19 detection, X-ray datasets are prioritized over CT scan datasets. The analysis reveals that X-rays and CT scans have surpassed all other imaging techniques. It has been observed that using CNNs yields a high degree of accuracy and practicability in identifying prominent lung diseases. Transfer learning and ensemble learning are complementary techniques to CNNs to facilitate analysis. Furthermore, accuracy is the most favored metric for assessment.
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Affiliation(s)
- Sunil Kumar
- Department of Computer Engineering, J. C. Bose University of Science and Technology, YMCA, Faridabad, India
- Department of Information Technology, School of Engineering and Technology (UIET), CSJM University, Kanpur, India
| | - Harish Kumar
- Department of Computer Engineering, J. C. Bose University of Science and Technology, YMCA, Faridabad, India
| | - Gyanendra Kumar
- Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India
| | | | - Anchit Bijalwan
- Faculty of Electrical and Computer Engineering, Arba Minch University, Arba Minch, Ethiopia.
| | - Manoj Diwakar
- Department of Computer Science and Engineering, Graphic Era Deemed to Be University, Dehradun, India
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Wang B, Qi J, An X, Wang Y. Heterogeneous fusion of biometric and deep physiological features for accurate porcine cough recognition. PLoS One 2024; 19:e0297655. [PMID: 38300934 PMCID: PMC10833553 DOI: 10.1371/journal.pone.0297655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 01/08/2024] [Indexed: 02/03/2024] Open
Abstract
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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Affiliation(s)
- Buyu Wang
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China
- Key Laboratory of Smart Animal Husbandry at Universities of Inner Mongolia Autonomous Region, Inner Mongolia Agricultural University, Inner Mongolia, China
| | - Jingwei Qi
- Key Laboratory of Smart Animal Husbandry at Universities of Inner Mongolia Autonomous Region, Inner Mongolia Agricultural University, Inner Mongolia, China
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China
| | - Xiaoping An
- Key Laboratory of Smart Animal Husbandry at Universities of Inner Mongolia Autonomous Region, Inner Mongolia Agricultural University, Inner Mongolia, China
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China
| | - Yuan Wang
- Key Laboratory of Smart Animal Husbandry at Universities of Inner Mongolia Autonomous Region, Inner Mongolia Agricultural University, Inner Mongolia, China
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China
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Hoffer O, Brzezinski RY, Ganim A, Shalom P, Ovadia-Blechman Z, Ben-Baruch L, Lewis N, Peled R, Shimon C, Naftali-Shani N, Katz E, Zimmer Y, Rabin N. Smartphone-based detection of COVID-19 and associated pneumonia using thermal imaging and a transfer learning algorithm. JOURNAL OF BIOPHOTONICS 2024:e202300486. [PMID: 38253344 DOI: 10.1002/jbio.202300486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/28/2023] [Accepted: 12/31/2023] [Indexed: 01/24/2024]
Abstract
COVID-19-related pneumonia is typically diagnosed using chest x-ray or computed tomography images. However, these techniques can only be used in hospitals. In contrast, thermal cameras are portable, inexpensive devices that can be connected to smartphones. Thus, they can be used to detect and monitor medical conditions outside hospitals. Herein, a smartphone-based application using thermal images of a human back was developed for COVID-19 detection. Image analysis using a deep learning algorithm revealed a sensitivity and specificity of 88.7% and 92.3%, respectively. The findings support the future use of noninvasive thermal imaging in primary screening for COVID-19 and associated pneumonia.
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Affiliation(s)
- Oshrit Hoffer
- School of Electrical Engineering, Afeka Tel Aviv Academic College of Engineering, Tel Aviv, Israel
| | - Rafael Y Brzezinski
- Neufeld Cardiac Research Institute, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Tamman Cardiovascular Research Institute, Leviev Heart Center, Sheba Medical Center Tel Hashomer, Ramat Gan, Israel
- Internal Medicine "C" and "E", Tel Aviv Medical Center, Tel Aviv, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Adam Ganim
- School of Electrical Engineering, Afeka Tel Aviv Academic College of Engineering, Tel Aviv, Israel
| | - Perry Shalom
- School of Software Engineering, Afeka Tel Aviv Academic College of Engineering, Tel Aviv, Israel
| | - Zehava Ovadia-Blechman
- School of Medical Engineering, Afeka Tel Aviv Academic College of Engineering, Tel Aviv, Israel
| | - Lital Ben-Baruch
- School of Electrical Engineering, Afeka Tel Aviv Academic College of Engineering, Tel Aviv, Israel
| | - Nir Lewis
- Neufeld Cardiac Research Institute, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Tamman Cardiovascular Research Institute, Leviev Heart Center, Sheba Medical Center Tel Hashomer, Ramat Gan, Israel
| | - Racheli Peled
- Neufeld Cardiac Research Institute, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Tamman Cardiovascular Research Institute, Leviev Heart Center, Sheba Medical Center Tel Hashomer, Ramat Gan, Israel
| | - Carmi Shimon
- School of Electrical Engineering, Afeka Tel Aviv Academic College of Engineering, Tel Aviv, Israel
| | - Nili Naftali-Shani
- Neufeld Cardiac Research Institute, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Tamman Cardiovascular Research Institute, Leviev Heart Center, Sheba Medical Center Tel Hashomer, Ramat Gan, Israel
| | - Eyal Katz
- School of Electrical Engineering, Afeka Tel Aviv Academic College of Engineering, Tel Aviv, Israel
| | - Yair Zimmer
- School of Medical Engineering, Afeka Tel Aviv Academic College of Engineering, Tel Aviv, Israel
| | - Neta Rabin
- Department of Industrial Engineering, Tel-Aviv University, Tel Aviv, Israel
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Fahad S, Li S, Zhai Y, Zhao C, Pikramenou Z, Wang M. Luminescence-Based Infrared Thermal Sensors: Comprehensive Insights. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2304237. [PMID: 37679096 DOI: 10.1002/smll.202304237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 07/08/2023] [Indexed: 09/09/2023]
Abstract
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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Affiliation(s)
- Shah Fahad
- School of Microelectronics, Southern University of Science and Technology, Shenzhen, 518055, P. R. China
- Engineering Research Center of Integrated Circuits for Next-Generation Communications, Ministry of Education, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Song Li
- Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, 518055, P. R. China
| | - Yufei Zhai
- School of Microelectronics, Southern University of Science and Technology, Shenzhen, 518055, P. R. China
| | - Cong Zhao
- School of Microelectronics, Southern University of Science and Technology, Shenzhen, 518055, P. R. China
- Engineering Research Center of Integrated Circuits for Next-Generation Communications, Ministry of Education, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Zoe Pikramenou
- School of Chemistry, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Min Wang
- School of Microelectronics, Southern University of Science and Technology, Shenzhen, 518055, P. R. China
- Engineering Research Center of Integrated Circuits for Next-Generation Communications, Ministry of Education, Southern University of Science and Technology, Shenzhen, 518055, China
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Payette J, Vaussenat F, Cloutier S. Deep learning framework for sensor array precision and accuracy enhancement. Sci Rep 2023; 13:11237. [PMID: 37433852 DOI: 10.1038/s41598-023-38290-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 07/06/2023] [Indexed: 07/13/2023] Open
Abstract
In the upcoming years, artificial intelligence is going to transform the practice of medicine in most of its specialties. Deep learning can help achieve better and earlier problem detection, while reducing errors on diagnosis. By feeding a deep neural network (DNN) with the data from a low-cost and low-accuracy sensor array, we demonstrate that it becomes possible to significantly improve the measurements' precision and accuracy. The data collection is done with an array composed of 32 temperature sensors, including 16 analog and 16 digital sensors. All sensors have accuracies between [Formula: see text]. 800 vectors are extracted, covering a range from to 30 to [Formula: see text]. In order to improve the temperature readings, we use machine learning to perform a linear regression analysis through a DNN. In an attempt to minimize the model's complexity in order to eventually run inferences locally, the network with the best results involves only three layers using the hyperbolic tangent activation function and the Adam Stochastic Gradient Descent optimizer. The model is trained with a randomly-selected dataset using 640 vectors (80% of the data) and tested with 160 vectors (20%). Using the mean squared error as a loss function between the data and the model's prediction, we achieve a loss of only 1.47x10[Formula: see text] on the training set and 1.22x10[Formula: see text] on the test set. As such, we believe this appealing approach offers a new pathway towards significantly better datasets using readily-available ultra low-cost sensors.
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Affiliation(s)
- Julie Payette
- Department of Electrical Engineering, École de technologie supérieure, Montréal, H3C 1K3, Canada
| | - Fabrice Vaussenat
- Department of Electrical Engineering, École de technologie supérieure, Montréal, H3C 1K3, Canada
| | - Sylvain Cloutier
- Department of Electrical Engineering, École de technologie supérieure, Montréal, H3C 1K3, Canada.
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