Thermography for disease detection in livestock: A scoping review

McManus, Rosemary; Boden, Lisa; Weir, William; Viora, Lorenzo; Barker, Robert N.; Kim, Yunhyong; McBride, P.K.; Yang, Shufan

doi:10.3389/fvets.2022.965622

Cited by 15 publications

(3 citation statements)

References 167 publications

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“…Thermal imaging can be used in livestock to detect indications of illness or injury. Changes in body temperature, for example, can suggest the existence of a fever, which is a typical sign of many disorders [ 11 ]. Thermal imaging can be used to detect estrus, which indicate when a female animal is in heat [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

DISubNet: Depthwise Separable Inception Subnetwork for Pig Treatment Classification Using Thermal Data

Colaco

Kim

Poulose

et al. 2023

Animals

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Thermal imaging is increasingly used in poultry, swine, and dairy animal husbandry to detect disease and distress. In intensive pig production systems, early detection of health and welfare issues is crucial for timely intervention. Using thermal imaging for pig treatment classification can improve animal welfare and promote sustainable pig production. In this paper, we present a depthwise separable inception subnetwork (DISubNet), a lightweight model for classifying four pig treatments. Based on the modified model architecture, we propose two DISubNet versions: DISubNetV1 and DISubNetV2. Our proposed models are compared to other deep learning models commonly employed for image classification. The thermal dataset captured by a forward-looking infrared (FLIR) camera is used to train these models. The experimental results demonstrate that the proposed models for thermal images of various pig treatments outperform other models. In addition, both proposed models achieve approximately 99.96–99.98% classification accuracy with fewer parameters.

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Section: Introductionmentioning

confidence: 99%

DISubNet: Depthwise Separable Inception Subnetwork for Pig Treatment Classification Using Thermal Data

Colaco

Kim

Poulose

et al. 2023

Animals

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“…In contrast, IRT is a noninvasive method that records thermal patterns released by the convolutional process in convolutional neural network (CNN) can reduce surface of the body. 7 By detecting variations in skin temperature, it can provide valuable insights into blood circulation and vascular irregularities associated with CVI. This method offers advantages such as being contactless, quick, and painless for patients.…”

Section: Introductionmentioning

confidence: 99%

“…However, ultrasound has certain limitations, such as operator dependency and a requirement for direct contact with the skin. In contrast, IRT is a non‐invasive method that records thermal patterns released by the convolutional process in convolutional neural network (CNN) can reduce surface of the body 7 . By detecting variations in skin temperature, it can provide valuable insights into blood circulation and vascular irregularities associated with CVI.…”

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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