An Approach for Thyroid Nodule Analysis Using Thermographic Images

González, José Ramón García; Rodrigues, Érick Oliveira; Damião, Charbel; Fontes, Cristina Asvolinsque Pantaleão; Silva, A. C.; Paiva, Afonso; Li, H.; Du, Chengcai; Conci, Aura

doi:10.1007/978-981-10-3147-2_26

Cited by 12 publications

(6 citation statements)

References 19 publications

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“…In second experiment, we develop an Artificial Neural Networks for automatics segmentation of the region with high temperature, waiting for it to coincide with the position of the nodule detected by ultrasonic test. The database used to train the ANN is available at https://visual.ic.uff.br/thyroid [13]. It comprises patients with thyroid nodules from the Outpatient Clinic of Endocrinology and Surgery at Hospital Universitário Antônio Pedro -HUAP, Fluminense Federal University (UFF).…”

Section: Methods 21 Databasementioning

confidence: 99%

“…Confirming that thermography presents greater accuracy in the detection of thyroid tumors. For their part, González et al [13] captured thermal images of the neck of patients with thyroid cancer and concluded that due to the greater metabolic activity of the thyroid tumor compared to healthy tissue, thermography could be used as a non-invasive tool for the detection of thyroid tumors.…”

Section: Introductionmentioning

confidence: 99%

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Thermal detection of thyroid pathologies using artificial neural networks

Flores Nuñez,

Jurado-Aguirre,

Pérez-Nájera

et al. 2023

Infrared Remote Sensing and Instrumentation XXXI

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Thyroid cancer is the most common type of neoplasm in the head and neck regions, thermal imaging has been used successfully for medical diagnostic purposes in areas such as breast cancer, wound care, vascular diseases, skin cancer and eye diseases. The thyroid is a richly vascularized gland that is located close to the skin, its hyper or hypo activity modifies the temperature pattern of the neck making thermography a good candidate to evaluate possible pathologies by digital infrared thermography. In this work an Artificial Neural Network based on the Nested U-Net architecture is trained and used to detect thyroid pathologies. Two thermographic databases were used to train the artificial neural network, the dataset was split into 80% for training and 20% for testing, with cross-validation used to evaluate the network's performance. The error between the predicted masks and the actual masks is calculated with the combination of Binary Cross Entropy (BCE) Loss and Dice Loss using Adam algorithm as training rule, with a cosine annealing schedule during 1500 epochs with a learning rate of 0.003 and a batch size of 6. Results show that when calculating the correlation coefficient between these deltas and the results of thyroid ultrasound expressed in the ACR TI-RADS classification, a high degree of correlation is obtained.

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Section: Methods 21 Databasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Thermal detection of thyroid pathologies using artificial neural networks

Flores Nuñez,

Jurado-Aguirre,

Pérez-Nájera

et al. 2023

Infrared Remote Sensing and Instrumentation XXXI

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“…Therefore, the development of other preoperative diagnostic techniques in these cases may avoid unnecessary diagnostic surgery. In line with this problem, Infrared Thermography has emerged as a low-cost and noninvasive method for investigating TN 6,7 . However, few studies have seriously evaluated this approach specially going up to the use of new Artificial Intelligence techniques, as the convolutional neural networks (CNNs), to promote a final conclusion on this subject as the here presented one 8 .…”

Section: P Damião 1* J R G Montero M B H Moran 2 R Amentioning

confidence: 99%

On the possibility of using temperature to aid in thyroid nodule investigation

Damião

Montero²,

Morán³

et al. 2020

Sci Rep

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Thyroid nodules are common, and their investigation is very important to exclude the possibility of cancer. The increase in blood vessels of malignant tumours may be related to local temperature augmentation detectable on the skin surface. The objective of this paper is to evaluate the feasibility of Infrared Thermography for cancer identification. For this purpose, two studies were performed. One used numerical modelling to simulate regional metabolic temperature propagation to evaluate whether a nodule is perceptible on the skin surface. A second study considered thyroid nodule identification by using convolutional neural networks (CNNs). First, variations in nodular size and fat thickness were investigated, showing that the fat layer has an important role in regional heat transfer. In the second study, the training process achieved accuracy of 96% for in-sample and 95% for validation. In the testing phase, 92% accuracy, 100% precision and 80% recall were achieved. Thus, the presented studies suggest the feasibility of using Infrared Thermography with the CNN Artificial Intelligence technique as additional information in the investigation of thyroid nodules for patients without a very thick subcutaneous fat layer.

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“…Moreover, Bahramian and Mojra also developed an artificial neural network for assessing correlations between the thermo-physical parameters of the thyroid and the temperature profile on the neck skin [9]. Furthermore, according to the findings of González et al, IRT applied to the neck region in patients with thyroid cancer revealed a higher metabolic activity in the tumor tissue compared to the healthy tissue [10]. The objective of this study is to examine the capability of IRT applied to the neck skin to distinguish TC, TA, and HC without the use of cold-stress protocols.…”

Section: Introductionmentioning

confidence: 99%