Designing of an inflammatory knee joint thermogram dataset for arthritis classification using deep convolution neural network.

Bardhan, Shawli; Nath, Satyabrata; Debnath, Tathagata; Bhattacharjee, Debotosh; Bhowmik, Mrinal Kanti

doi:10.1080/17686733.2020.1855390

Cited by 16 publications

(4 citation statements)

References 39 publications

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“…CNN networks can also be used to segment patients’ breasts in thermal images and classify pathologies [ 28 ]. Reference [ 29 ] demonstrates the results of using CNN networks to classify thermograms of healthy and arthritis-affected knees. All these applications demonstrate the potential of deep learning algorithms in biomedical applications based on thermal imaging.…”

Section: Introductionmentioning

confidence: 99%

Thermal Time Constant CNN-Based Spectrometry for Biomedical Applications

Strąkowska

Strzelecki

2023

Sensors

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This paper presents a novel method based on a convolutional neural network to recover thermal time constants from a temperature–time curve after thermal excitation. The thermal time constants are then used to detect the pathological states of the skin. The thermal system is modeled as a Foster Network consisting of R-C thermal elements. Each component is represented by a time constant and an amplitude that can be retrieved using the deep learning system. The presented method was verified on artificially generated training data and then tested on real, measured thermographic signals from a patient suffering from psoriasis. The results show proper estimation both in time constants and in temperature evaluation over time. The error of the recovered time constants is below 1% for noiseless input data, and it does not exceed 5% for noisy signals.

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Section: Introductionmentioning

confidence: 99%

Thermal Time Constant CNN-Based Spectrometry for Biomedical Applications

Strąkowska

Strzelecki

2023

Sensors

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“…In India, 15% of the adult population had arthritis in 2015, compared with 22.7% in the United States [ 2 ]. According to medical research, there are around 100 different types of arthritis, with rheumatoid arthritis (RA) and osteoarthritis (OA) being the most frequent [ 3 ]. Medical imaging is a growing field and has several tools and methods for getting information from medical images.…”

Section: Introductionmentioning

confidence: 99%

A Framework of Faster CRNN and VGG16-Enhanced Region Proposal Network for Detection and Grade Classification of Knee RA

et al. 2023

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We developed a framework to detect and grade knee RA using digital X-radiation images and used it to demonstrate the ability of deep learning approaches to detect knee RA using a consensus-based decision (CBD) grading system. The study aimed to evaluate the efficiency with which a deep learning approach based on artificial intelligence (AI) can find and determine the severity of knee RA in digital X-radiation images. The study comprised people over 50 years with RA symptoms, such as knee joint pain, stiffness, crepitus, and functional impairments. The digitized X-radiation images of the people were obtained from the BioGPS database repository. We used 3172 digital X-radiation images of the knee joint from an anterior–posterior perspective. The trained Faster-CRNN architecture was used to identify the knee joint space narrowing (JSN) area in digital X-radiation images and extract the features using ResNet-101 with domain adaptation. In addition, we employed another well-trained model (VGG16 with domain adaptation) for knee RA severity classification. Medical experts graded the X-radiation images of the knee joint using a consensus-based decision score. We trained the enhanced-region proposal network (ERPN) using this manually extracted knee area as the test dataset image. An X-radiation image was fed into the final model, and a consensus decision was used to grade the outcome. The presented model correctly identified the marginal knee JSN region with 98.97% of accuracy, with a total knee RA intensity classification accuracy of 99.10%, with a sensitivity of 97.3%, a specificity of 98.2%, a precision of 98.1%, and a dice score of 90.1% compared with other conventional models.

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“…In recent years, IRT technology has undergone significant advancements and demonstrates potential across a spectrum of applications [ 15 ]. These applications include cancer detection [ 16 ], sports medicine [ 17 ], foot thermoregulation study [ 18 ], sinusitis detection [ 19 ], arthritis diagnosis [ 20 ], neonatal disease detection [ 21 , 22 ], multiple sclerosis evaluation [ 23 ], evaluation of diabetes-associated vascular disorders [ 24 ], dermatology [ 25 ], pain monitoring [ 26 ], dentistry [ 27 ], surgical procedures [ 28 ], etc. In this study, our focus is on the use of IRTs for EBT measurements.…”

Section: Introductionmentioning

confidence: 99%

Best Practices for Body Temperature Measurement with Infrared Thermography: External Factors Affecting Accuracy

Mazdeyasna,

Ghassemi,

Wang

2023

Sensors

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Infrared thermographs (IRTs) are commonly used during disease pandemics to screen individuals with elevated body temperature (EBT). To address the limited research on external factors affecting IRT accuracy, we conducted benchtop measurements and computer simulations with two IRTs, with or without an external temperature reference source (ETRS) for temperature compensation. The combination of an IRT and an ETRS forms a screening thermograph (ST). We investigated the effects of viewing angle (θ, 0–75°), ETRS set temperature (TETRS, 30–40 °C), ambient temperature (Tatm, 18–32 °C), relative humidity (RH, 15–80%), and working distance (d, 0.4–2.8 m). We discovered that STs exhibited higher accuracy compared to IRTs alone. Across the tested ranges of Tatm and RH, both IRTs exhibited absolute measurement errors of less than 0.97 °C, while both STs maintained absolute measurement errors of less than 0.12 °C. The optimal TETRS for EBT detection was 36–37 °C. When θ was below 30°, the two STs underestimated calibration source (CS) temperature (TCS) of less than 0.05 °C. The computer simulations showed absolute temperature differences of up to 0.28 °C and 0.04 °C between estimated and theoretical temperatures for IRTs and STs, respectively, considering d of 0.2–3.0 m, Tatm of 15–35 °C, and RH of 5–95%. The results highlight the importance of precise calibration and environmental control for reliable temperature readings and suggest proper ranges for these factors, aiming to enhance current standard documents and best practice guidelines. These insights enhance our understanding of IRT performance and their sensitivity to various factors, thereby facilitating the development of best practices for accurate EBT measurement.

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Designing of an inflammatory knee joint thermogram dataset for arthritis classification using deep convolution neural network.

Cited by 16 publications

References 39 publications

Thermal Time Constant CNN-Based Spectrometry for Biomedical Applications

Thermal Time Constant CNN-Based Spectrometry for Biomedical Applications

A Framework of Faster CRNN and VGG16-Enhanced Region Proposal Network for Detection and Grade Classification of Knee RA

Best Practices for Body Temperature Measurement with Infrared Thermography: External Factors Affecting Accuracy

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