Advances in Breast Thermography

Kakileti, Siva Teja; Manjunath, Geetha; Madhu, Himanshu; Ramprakash, H V

doi:10.5772/intechopen.69198

Cited by 34 publications

(11 citation statements)

References 40 publications

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“…Furthermore, mammography has difficulties in reading a large, dense, and fibrocystic breasts. As discussed in a previous study [48], to reliably detect tumors using Xrays, the density of the lump should be more than that of the surrounding tissue. Consequently, mammography is mainly applicable to a woman aged above 45-years.…”

Section: A Potential Of Breast Thermogram Modalitymentioning

confidence: 96%

A Review on Recent Progress in Thermal Imaging and Deep Learning Approaches for Breast Cancer Detection

et al. 2020

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Developing a breast cancer screening method is very important to facilitate early breast cancer detection and treatment. Building a screening method using medical imaging modality that does not cause body tissue damage (non-invasive) and does not involve physical touch is challenging. Thermography, a non-invasive and non-contact cancer screening method, can detect tumors at an early stage even under precancerous conditions by observing temperature distribution in both breasts. The thermograms obtained on thermography can be interpreted using deep learning models such as convolutional neural networks (CNNs). CNNs can automatically classify breast thermograms into categories such as normal and abnormal. Despite their demostrated utility, CNNs have not been widely used in breast thermogram classification. In this study, we aimed to summarize the current work and progress in breast cancer detection based on thermography and CNNs. We first discuss of breast thermography potential in early breast cancer detection, providing an overview of the availability of breast thermal datasets together with publicly accessible. We also discuss characteristics of breast thermograms and the differences between healthy and cancerous thermographic patterns. Breast thermogram classification using a CNN model is described step by step including a simulation example illustrating feature learning. We cover most research related to the implementation of deep neural networks for breast thermogram classification and propose future research directions for developing representative datasets, feeding the segmented image, assigning a good kernel, and building a lightweight CNN model to improve CNN performance. INDEX TERMS breast cancer; convolutional neural network; deep learning; early detection; thermogram

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Section: A Potential Of Breast Thermogram Modalitymentioning

confidence: 96%

A Review on Recent Progress in Thermal Imaging and Deep Learning Approaches for Breast Cancer Detection

et al. 2020

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“…Finalmente, pode-se mencionar os trabalhos de Santana et al [5], Mambou et al [15] e Kakileti et al [14] que, ainda que não estejam diretamente relacionados com a segmentação de imagens das mamas usando protocolo lateral, são propostas recentes importantes para a literatura. Respectivamente, o primeiroé um estudo profundo sobre classificação de imagens de infravermelho (inclusive com o protocolo lateral), o segundo aplica a classificação diagnóstica utilizando Aprendizado Profundo (Deep Learning) e oúltimo descreve os avanços recentes envolvendo diagnóstico de imagens infravermelhas das mamas em formato survey.…”

Section: Trabalhos Relacionadosunclassified

Unsupervised Segmentation of Breast Infrared Images in Lateral View Using Histogram of Oriented Gradients

Correa¹,

Oliveira²,

Baffa³

et al. 2020

Anais Do XVI Workshop De Visão Computacional (WVC 2020)

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Breast cancer is the second most common type of cancer in the world. It is estimated that 29.7% of new cases diagnosed in Brazil occur in any structures of the breasts. However, the disease has a good prognosis if detected early. Thus, the development of new technologies to help doctors to provide an accurate diagnosis is indispensable. The goal of this work is to develop a new method to automate parts of computer-aided diagnosis systems, performing the unsupervised segmentation of the Region of Interest (ROI) of infrared breast images acquired in lateral view. The segmentation proposed in this paper consists of three stages. The first stage pre-processes the infrared images of the lateral region of breasts. Later, features are extracted from a descriptor based on Histogram of Oriented Gradients (HOG). Concluding, a Machine Learning algorithm is used to perform the segmentation of the sample. The current method obtained an average of 89.9% accuracy and 94.3% specificity in our experiments, which is promising compared to other works.

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“…Any abnormality such as tumor or inflammation can cause the atypical distribution of temperature on the skin surface. Tumors have higher heat generation rates compared with surrounding healthy tissues, thus allowing for the observation of non-homogeneity in breast thermograms with tumors [10][11][12][13][14][15][16][17][18][19][20][21][22].…”

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

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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.

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Advances in Breast Thermography

Cited by 34 publications

References 40 publications

A Review on Recent Progress in Thermal Imaging and Deep Learning Approaches for Breast Cancer Detection

A Review on Recent Progress in Thermal Imaging and Deep Learning Approaches for Breast Cancer Detection

Unsupervised Segmentation of Breast Infrared Images in Lateral View Using Histogram of Oriented Gradients

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

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