Detection of Breast Abnormality Using Rotational Thermography

Francis, Sheeja V.; Sasikala, M.; Jaipurkar, Sandeep D.

doi:10.1007/978-981-10-3147-2_9

Cited by 10 publications

(4 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the most common causes of disease-related death among young women in developing countries is breast cancer [1]. Changes in a cell genome can be caused by various factors such as hormonal dysfunctions or external causes, which lead to the development of cancerous cells.…”

Section: Introductionmentioning

confidence: 99%

Patient/Breast-Specific Detection of Breast Tumor Based on Patients’ Thermograms, 3D Breast Scans, and Reverse Thermal Modelling

et al. 2021

View full text Add to dashboard Cite

Background: Mammography is the preferred method for the diagnosis of breast cancer. However, this diagnostic technique fails to detect tumors of small sizes, and it does not work well for younger patients with high breast tissue density. Methods: This paper proposes a novel tool for the early detection of breast cancer, which is patient-specific, non-invasive, inexpensive, and has potential in terms of accuracy compared with existing techniques. The main principle of this method is based on the use of temperature contours from breast skin surfaces through thermography, and inverse thermal modeling based on Finite Element Analysis (FEA) and a Genetic Algorithm (GA)-based optimization tool to estimate the depths and sizes of tumors as well as patient/breast-specific tissue properties. Results: The study was conducted by using a 3D geometry of patients’ breasts and their temperature contours, which were clinically collected using a 3D scanner and a thermal imaging infrared (IR) camera. Conclusion: The results showed that the combination of 3D breast geometries, thermal images, and inverse thermal modeling is capable of estimating patient/breast-specific breast tissue and physiological properties such as gland and fat contents, tissue density, thermal conductivity, specific heat, and blood perfusion rate, based on a multilayer model consisting of gland and fat. Moreover, this tool was able to calculate the depth and size of the tumor, which was validated by the doctor’s diagnosis.

show abstract

Section: Introductionmentioning

confidence: 99%

Patient/Breast-Specific Detection of Breast Tumor Based on Patients’ Thermograms, 3D Breast Scans, and Reverse Thermal Modelling

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Body temperature is commonly used as a health indicator and thermal data had been applied for the diagnosis of many illnesses [1]. Recently, thermal patterns had been suggested for the early detection of breast tumours [2][3][4]. This technique is relatively inexpensive, non-invasive, and temperature sensors can be easily embedded into wearable computers for continuous real-time monitoring of health status.…”

Section: Introductionmentioning

confidence: 99%

Detection of Tumours Using Breast Surface Thermal Patterns

Yu¹,

Hu²,

Fok³

2021

World Congress on Mechanical, Chemical, and Material Engineering

View full text Add to dashboard Cite

Influencing factors are important considerations in the application of non-invasive thermal diagnostics for the early detection of breast tumour. In this paper, experimental studies of artificial tumours embedded inside silicone breasts coupled with numerical simulations using 3D finite-element method in ANSYS were used to investigate the effects of tissue conductivity, tumour size and tumour depth on the heat patterns at the breast skin surface. After validating the numerical breast model, the analysis was extended to examine the heat patterns of a growing tumour. The findings revealed that thermal patterns of the breast surface over time could be useful for the detection of tumours. The existence of tumours would be more noticeable from thermal images of breasts with more fatty tissues, and breasts of lower density. The method would be more suitable for the detection of large tumours near the skin surface. The simulated results suggested that it is possible to detect an initial 4 mm HER2-positive tumour at a depth of 44 mm after about 196 days when it had grown to 7 mm using a temperature sensor with resolution of 0.01 ℃.

show abstract

“…The implementation of this joint approach can be encountered in the detection of thyroid cancer, extreme fatigue, rheumatoid arthritis, hypertension and psoriasis, but, not to our knowledge, to melanoma and nevi classification. Nonetheless, it is consensus the positive contribution of AI in medical diagnosis, particularly in rural areas where lack of specialized healthcare professionals occurs …”

Section: Introductionmentioning

confidence: 99%

“…Nonetheless, it is consensus the positive contribution of AI in medical diagnosis, particularly in rural areas where lack of specialized healthcare professionals occurs. 24 Here, it is presented a study focused on the use of static and dynamically acquired thermograms with the aim of differentiating melanoma tumours from common melanocytic nevi, using image analysis strategies and machine learning classifiers.…”

Section: Introductionmentioning

confidence: 99%

Distinguishing melanocytic nevi from melanomas using static and dynamic infrared thermal imaging

Magalhães

Vardasca

Rebelo

et al. 2019

Acad Dermatol Venereol

View full text Add to dashboard Cite

Background The incidence rates of melanoma have risen to worrying levels over the last decade. Delayed diagnosis, due to faults on the detection stage, indicates the necessity of new aiding diagnosis techniques. Since metabolic activity is highly connected to neoplasia formation, a detection technique that focuses its results on vascular responses, as Infrared thermal (IRT), seems to be a viable option. Materials and methods Static and dynamic (cooling) thermal images of melanoma and melanocytic nevi lesions were collected and analysed to retrieve thermal parameters characteristic of this skin lesion types. The steady‐state and dynamic variables were tested separately with different machine learning classifiers to verify whether the distinction of melanoma and nevi lesions was achievable. Results The differentiation of both types of skin tumours was doable, achieving an accuracy of 84.2% and a sensitivity of 91.3% with the implementation of a learner based on support vector machines and an input vector composed by static variables. Conclusion The use of IRT for skin tumour classification is achievable, but some improvement is needed to raise the metrics of sensitivity and specificity. For future work, it is recommended the study of dynamic parameters for the classification of other types of skin neoplasia.

show abstract

Detection of Breast Abnormality Using Rotational Thermography

Cited by 10 publications

References 31 publications

Patient/Breast-Specific Detection of Breast Tumor Based on Patients’ Thermograms, 3D Breast Scans, and Reverse Thermal Modelling

Patient/Breast-Specific Detection of Breast Tumor Based on Patients’ Thermograms, 3D Breast Scans, and Reverse Thermal Modelling

Detection of Tumours Using Breast Surface Thermal Patterns

Distinguishing melanocytic nevi from melanomas using static and dynamic infrared thermal imaging

Contact Info

Product

Resources

About