A Review on Thermal Imaging-Based Breast Cancer Detection Using Deep Learning

Tsietso, Dennies; Yahya, Abid; Samikannu, Ravi

doi:10.1155/2022/8952849

Cited by 7 publications

(3 citation statements)

References 123 publications

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“…Thermal imaging systems utilize these principles to detect and capture infrared radiation emitted by objects [15] . The imaging sensors in these systems are typically composed of an array of detectors, known as microbolometers, which convert infrared radiation into electrical signals [16] . The signals are then processed and mapped to produce a thermal image that represents the temperature distribution across the object's surface.…”

Section: Principles Of Thermal Imagingmentioning

confidence: 99%

Thermal imaging for cancer detection

Aggarwal

2023

Imaging. Radiat. Res.

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Problem: There is a need for effective and non-invasive techniques for early cancer detection to improve treatment outcomes and patient care. Motivation: This research explores the potential of thermal imaging as a non-invasive technique for cancer detection. Aim: The aim of this study is to investigate thermal imaging as a valuable tool for early cancer detection and its potential to enhance treatment outcomes and patient care. Methodology: The paper discusses the principles of thermal imaging, its advantages and limitations, and its application to various types of cancer. It also presents a review of recent studies in the field. Main results: The findings suggest that thermal imaging holds promise as a valuable tool for early cancer detection. Further impact of those results: The potential application of thermal imaging in cancer detection could lead to improved treatment outcomes and enhance overall patient care. The article also highlights the challenges and future prospects of thermal imaging in this domain.

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Section: Principles Of Thermal Imagingmentioning

confidence: 99%

Thermal imaging for cancer detection

Aggarwal

2023

Imaging. Radiat. Res.

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“…In the literature, several methods have been reported to automate this task (breast segmentation), mainly based on traditional computer vision approaches, and recently taking advantage of machine learning algorithms, for example, Tsietso et al and Michael et al review the main approaches. 25,26 Canny edge detector-based algorithms stand out, evidently due to breast pattern characteristics (high contrast on down and lateral borders). 27,28 Sanieny et al 29 evaluated the edge detection as connected components to find the two curves of maximum concavity (breast boundaries bottom), fitting a circle in it, which defines the top of the breast.…”

Section: Introductionmentioning

confidence: 99%

Breast segmentation in infrared thermography from characteristical inframammary shape

Alvarez‐Padilla,

Flores‐Nunez,

Alvarez‐Padilla

et al. 2024

Numer Methods Biomed Eng

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Infrared thermography is gaining relevance in breast cancer assessment. For this purpose, breast segmentation in thermograms is an important task for performing automatic image analysis and detecting possible temperature changes that indicate the presence of malignancy. However, it is not a simple task since the breast limit borders, especially the top borders, often have low contrast, making it difficult to isolate the breast area. Several algorithms have been proposed for breast segmentation, but these highly depend on the contrast at the lower breast borders and on filtering algorithms to remove false edges. This work focuses on taking advantage of the distinctive inframammary shape to simplify the definition of the lower breast border, regardless of the contrast level, which indeed also provides a strong anatomical reference to support the definition of the poorly marked upper boundary of the breasts, which has been one of the major challenges in the literature. In order to demonstrate viability of the proposed technique for an automatic breast segmentation, we applied it to a database with 180 thermograms and compared their results with those reported by others in the literature. We found that our approach achieved a high performance, in terms of Intersection over Union of 0.934, even higher than that reported by artificial intelligence algorithms. The performance is invariant to breast sizes and thermal contrast of the images.

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“…Thermography may be used in diagnosing breast cancer and treatment, especially in estimating radiation reactions after radiotherapy [ 25 ]. For thermal imaging as breast cancer screening, we refer the reader to review articles [ 26 , 27 , 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Vascular Imaging Using Active Breast Thermography

Gershenson

2023

Sensors

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Mammography is considered the gold standard for breast cancer screening and diagnostic imaging; however, there is an unmet clinical need for complementary methods to detect lesions not characterized by mammography. Far-infrared ‘thermogram’ breast imaging can map the skin temperature, and signal inversion with components analysis can be used to identify the mechanisms of thermal image generation of the vasculature using dynamic thermal data. This work focuses on using dynamic infrared breast imaging to identify the thermal response of the stationary vascular system and the physiologic vascular response to a temperature stimulus affected by vasomodulation. The recorded data are analyzed by converting the diffusive heat propagation into a virtual wave and identifying the reflection using component analysis. Clear images of passive thermal reflection and thermal response to vasomodulation were obtained. In our limited data, the magnitude of vasoconstriction appears to depend on the presence of cancer. The authors propose future studies with supporting diagnostic and clinical data that may provide validation of the proposed paradigm.

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A Review on Thermal Imaging-Based Breast Cancer Detection Using Deep Learning

Cited by 7 publications

References 123 publications

Thermal imaging for cancer detection

Thermal imaging for cancer detection

Breast segmentation in infrared thermography from characteristical inframammary shape

Dynamic Vascular Imaging Using Active Breast Thermography

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