The use of infrared thermal imaging in the diagnosis of deep vein thrombosis

Kacmaz, Seydi; Erçelebi, Ergün; Zengin, Suat; Cindoruk, Şener

doi:10.1016/j.infrared.2017.09.005

Cited by 38 publications

(20 citation statements)

References 36 publications

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“…The size of the images was 640*480 pixels. Further, IR spectroscopy has unique usage in the study of tissue breakage, which makes it a suitable choice for the study of iridology-based disease diagnosis [55]. Sample images for both healthy subjects and subjects with chronic liver disease are given in Fig.…”

Section: Eye Image Acquisitionmentioning

confidence: 99%

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Notice of Retraction: Infrared Sensing Based Non-Invasive Initial Diagnosis of Chronic Liver Disease Using Ensemble Learning

et al. 2021

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The liver is a vital human body organ and its functionality can be degraded by several diseases such as hepatitis, fatty liver disease, and liver cancer and so forth. Hence, the early diagnosis of liver diseases is extremely crucial for saving human lives. With the rapid development of multimedia technology, it is now possible to design and implement a non-invasive system that can chronic liver diseases. For this purpose, machine learning and Artificial Intelligence (AI) have been used within the past few years. In this regard, digital image processing supported by AI methods has been implemented in the diagnosis of diseases that also showed high reliability. Therefore, in this paper, an iris feature-based non-invasive technique is proposed by incorporating a novel machine-learning algorithm. The experimental setup involved data set for the models' training included 879 subjects from Pakistan, of which 453 subjects have chronic liver disease and 426 are healthy. The iris images were collected using an infrared camera that consists of a lens, a thermal sensor and digital electronics processing. The lens focuses on the infrared energy on the sensor, using distinctive forms of features twenty-two physiological and thirty-three iris features. The designed classification model for a non-invasive system combined eleven different classifiers and used cross-validation techniques for comparing the results. The overall performance of the model was analyzed using five parameters: accuracy, precision, F-score, specificity, and sensitivity. The results confirmed that the proposed non-invasive model is capable of predicting chronic liver diseases with 98% of accuracy.

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Section: Eye Image Acquisitionmentioning

confidence: 99%

“…The presented ensemble model for non-invasive early di-agnosis of liver disease has incorporated a novel approach of diagnosis using iris and physiological features. Overall, the feature vector (FV) consists of 55…”

Section: Introductionmentioning

confidence: 99%

Notice of Retraction: Infrared Sensing Based Non-Invasive Initial Diagnosis of Chronic Liver Disease Using Ensemble Learning

et al. 2021

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“…Secondly, although using GLCM could obtain several texture features from images, entropy and variance are the most distinguishable features to the infrared thermal images [8], [33], which is also accord from FIGURE 5. FIGURE 5 presents the mean of the 5 texture features extracted from the normal dataset, the PI dataset, and the test dataset.…”

Section: Resultsmentioning

confidence: 73%

Infrared Thermal Images Classification for Pressure Injury Prevention Incorporating the Convolutional Neural Networks

Wang

Jiang

et al. 2021

IEEE Access

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Hospital-acquired pressure injury is difficult to identify in the early stage, accompanied with increased morbidity but considered to be preventable. For helping the nurses to monitor the status of the patients' skin, the infrared thermal imaging and the convolutional neural networks were integrated to identify and prevent pressure injury. In the first stage, infrared thermal images were shoot and labelled with the normal group and the pressure injury group by the clinical nurses. In the second stage, the convolutional neural networks and two machine learning algorithms, the random forest and the support vector machine, were applied to classify these two classes of the collected images. The classification model was trained on 164 images and was tested on the special image dataset consisted of 82 infrared thermal images of 1 day before pressure injury. Gray level co-occurrence matrix was utilized to extract the texture features of the infrared thermal images and we chose the pearson correlation coefficient and the Chi square test as the feature selection methods. The classification accuracy of the proposed convolutional neural networks model was 95.2% and the area under curve was 0.98. Moreover, the classification results from the test dataset were conformed to the experience of the experts. After feature selection, variance and entropy were proved to the best distinguishable features. Finally, we concluded that combining the infrared thermal imaging and convolutional neural networks could contribute to the prevention of pressure injury. This measure should be performed in high-risk populations to reduce the incidence of pressure injury.INDEX TERMS Convolutional neural networks, thermal image, pressure injury.

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“…Infrared thermography is a non-invasive technique [27] in which infrared thermal images are used to detect the DFU using different computer vision methodologies [28]. Smartphone health applications are becoming more popular to monitor the crucial aspects related to the human body [29], FootSnap application is used to create a standard DFU dataset [30] and MyFootCare gives suitable guidance related to the DFU patients. Few Apps facilitate the users to crop the patches of the images and also employ the color based clustering methodologies to segment the DFU [31], but these methods do not accurately segment the complete infected part of the human foot.…”

Section: Related Workmentioning

confidence: 99%

An Integrated Design for Classification and Localization of Diabetic Foot Ulcer Based on CNN and YOLOv2-DFU Models

et al. 2020

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The use of infrared thermal imaging in the diagnosis of deep vein thrombosis

Cited by 38 publications

References 36 publications

Notice of Retraction: Infrared Sensing Based Non-Invasive Initial Diagnosis of Chronic Liver Disease Using Ensemble Learning

Notice of Retraction: Infrared Sensing Based Non-Invasive Initial Diagnosis of Chronic Liver Disease Using Ensemble Learning

Infrared Thermal Images Classification for Pressure Injury Prevention Incorporating the Convolutional Neural Networks

An Integrated Design for Classification and Localization of Diabetic Foot Ulcer Based on CNN and YOLOv2-DFU Models

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