Fat-based studies for computer-assisted screening of child obesity using thermal imaging based on deep learning techniques: a comparison with quantum machine learning approach

Rashmi, Richa; Snekhalatha, U.; Krishnan, Palani Thanaraj; Dhanraj, Varun

doi:10.1007/s00500-021-06668-3

Cited by 13 publications

(5 citation statements)

References 51 publications

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Section: Methodology Study Subjectsmentioning

confidence: 99%

“…We acquired the tongue thermal and visible images from 160 recruited participants. Based on the diabetes diagnostic criteria by ADA 15 , the study subjects (N = 160) are categorized into two groups, namely. Group I: Normal (N = 80), comprised of age and sex-matched subjects with a Male: Female ratio of 1:2, and a mean age ± standard deviation of 41.23 ± 10.82 years.…”

Section: Methodsmentioning

confidence: 99%

“…The emissivity of the tongue, when using an infrared thermal camera, is often set to a standard value of around 0.98, corresponding to the emissivity of human skin. The participants are requested to be seated for 15 www.nature.com/scientificreports/ the fasting condition. The distance between the camera and the subject's tongue is consistently maintained at 0.3 m 34 .…”

Section: Thermal and Visible Tongue Images Acquisition And Analysismentioning

confidence: 99%

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Tongue image fusion and analysis of thermal and visible images in diabetes mellitus using machine learning techniques

Thirunavukkarasu,

Umapathy,

Ravi

et al. 2024

Sci Rep

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The study aimed to achieve the following objectives: (1) to perform the fusion of thermal and visible tongue images with various fusion rules of discrete wavelet transform (DWT) to classify diabetes and normal subjects; (2) to obtain the statistical features in the required region of interest from the tongue image before and after fusion; (3) to distinguish the healthy and diabetes using fused tongue images based on deep and machine learning algorithms. The study participants comprised of 80 normal subjects and age- and sex-matched 80 diabetes patients. The biochemical tests such as fasting glucose, postprandial, Hba1c are taken for all the participants. The visible and thermal tongue images are acquired using digital single lens reference camera and thermal infrared cameras, respectively. The digital and thermal tongue images are fused based on the wavelet transform method. Then Gray level co-occurrence matrix features are extracted individually from the visible, thermal, and fused tongue images. The machine learning classifiers and deep learning networks such as VGG16 and ResNet50 was used to classify the normal and diabetes mellitus. Image quality metrics are implemented to compare the classifiers’ performance before and after fusion. Support vector machine outperformed the machine learning classifiers, well after fusion with an accuracy of 88.12% compared to before the fusion process (Thermal-84.37%; Visible-63.1%). VGG16 produced the classification accuracy of 94.37% after fusion and attained 90.62% and 85% before fusion of individual thermal and visible tongue images, respectively. Therefore, this study results indicates that fused tongue images might be used as a non-contact elemental tool for pre-screening type II diabetes mellitus.

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Section: Methodology Study Subjectsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Thermal and Visible Tongue Images Acquisition And Analysismentioning

confidence: 99%

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Tongue image fusion and analysis of thermal and visible images in diabetes mellitus using machine learning techniques

Thirunavukkarasu,

Umapathy,

Ravi

et al. 2024

Sci Rep

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“…Infrared thermography (IRT) and ultrasound are both diagnostic tools widely used in the medical field for evaluating various conditions. 4,5 In the case of CVI, these techniques play a crucial role in detecting and assessing the severity of this condition. While ultrasound has traditionally been the gold standard for diagnosing CVI in lower extremity, IRT is emerging as a promising alternative.…”

Section: Introductionmentioning

confidence: 99%

“…Infrared thermography (IRT) and ultrasound are both diagnostic tools widely used in the medical field for evaluating various conditions 4,5 . In the case of CVI, these techniques play a crucial role in detecting and assessing the severity of this condition.…”

Section: Introductionmentioning

confidence: 99%

CVINet: A deep learning based model for the diagnosis of chronic venous insufficiency in lower extremity using infrared thermal images

Krishnan,

Muthu

2023

Int J Imaging Syst Tech

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Chronic venous insufficiency (CVI) is a venous disorder characterized by impaired blood flow from the lower extremities back to the heart, leading to various symptoms and complications. Accurate and timely diagnosis of CVI is critical for effective management and prevention of further complications. Deep learning (DL) models have shown results that are promising in the field of medical image analysis tasks over the past few years. This article presents CVINet, a enhanced DL‐based convolutional neural network (CNN) model that has been developed specifically for the purpose of diagnosing CVI utilizing thermal imaging data. The proposed CNN model extracts relevant features from input images using a multi‐layered architecture with convolutional layers and pooling layers. Following feature extraction, data are directed to fully connected layers for performing classification task. To enhance the performance of this model, we employ techniques such as dropout regularization and batch normalization. This model utilizes its ability to automatically learn discriminative features from raw thermal images. A large set of thermal images from both CVI patients and healthy individuals was acquired for training and testing of the model. A comparison between few conventional pre‐trained models was conducted, and the best performing model for classification was examined. Pre‐trained VGG‐16, EfficientNet‐B0, and ResNet‐152 models produced classification accuracies of 94.7%, 95.3%, and 95.8%, respectively. The Proposed CVINet model attained a classification accuracy of 96.8% on binary classification, demonstrating high performance compared with other state‐of‐the‐art networks. Overall, our proposed CNN model offers a promising solution for accurately classifying CVI and normal subjects based on thermal images which outperforms the diagnosis by clinician. The potential benefits include early detection of CVI and personalized treatment strategies that can improve patient outcomes. They improve the accuracy of patient diagnosis, reducing radiologist workload and providing them with a tool that can automatically assess the condition of the lower limb venous condition, lowering the risk of misdiagnosis. Furthermore, improving healthcare service quality and early detection can alter the course of the disease and reduce the morbidity and mortality rate of the disease.

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Review of computer‐aided methods in fat crystallization studies

Qi,

Zhang

2024

J Americ Oil Chem Soc

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With recent improvements in computer performance, computer‐aided studies have become increasingly important. Computer‐aided methods have been applied in fat crystallization studies for modeling, simulation, optimization, data analysis and visualization. In this paper, various methods, such as molecular dynamic simulation, Monte Carlo, cellular automata modeling, finite element analysis, machine learning and computer vision, are introduced. Applications and advances in mechanism explanation, behavior prediction, process optimization, and so forth, are reviewed for fat crystallization. As a powerful and essential tool, computer‐aided study should play an important role in the field of lipid research in the future.

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Fat-based studies for computer-assisted screening of child obesity using thermal imaging based on deep learning techniques: a comparison with quantum machine learning approach

Cited by 13 publications

References 51 publications

Tongue image fusion and analysis of thermal and visible images in diabetes mellitus using machine learning techniques

Tongue image fusion and analysis of thermal and visible images in diabetes mellitus using machine learning techniques

CVINet: A deep learning based model for the diagnosis of chronic venous insufficiency in lower extremity using infrared thermal images

Review of computer‐aided methods in fat crystallization studies

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