Thermal modeling for breast tumor detection using thermography

Mukhmetov, Olzhas; Igali, Dastan; Mashekova, A. S.; Zhao, Yong; Ng, E. Y. K.; Fok, S.C.; Teh, Soo Lee

doi:10.1016/j.ijthermalsci.2020.106712

Cited by 14 publications

(7 citation statements)

References 24 publications

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“…As the tumor size increases, so does the surface temperature [26]. is view is supported by the authors in [27], who concluded that a tumor inside a breast could give a varying temperature profile depending on factors such as size, location, and depth. is can help detect breast cancer early, particularly in asymptomatic people.…”

Section: Breast Thermographymentioning

confidence: 95%

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

Tsietso

Yahya

Samikannu

2022

Mobile Information Systems

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Breast cancer is the most common form of cancer in women. Its aggressive nature has made it one of the chief factors of high female mortality. Therefore, this has motivated research to achieve early diagnosis since it is the best strategy for patient survival. Currently, mammography is the gold standard for detecting breast cancer. However, it is expensive, unsuitable for dense breasts, and an invasive process that exposes the patient to radiation. Infrared thermography is gaining popularity as a screening modality for the early detection of breast cancer. It is a noninvasive and cost-effective modality that allows health practitioners to observe the temperature profile of the breast region for signs of cancerous tumors. Deep learning has emerged as a powerful computational tool for the early detection of breast cancer in radiology. As such, this study presents a review that shows existing work on deep learning-based Computer-aided Diagnosis (CADx) systems for breast cancer detection. In the same context, it reflects on classification utilizing breast thermograms. It first provides an overview of infrared thermography, details on available breast thermogram datasets, and then segmentation techniques applied to these thermograms. We also provide a brief overview of deep neural networks. Finally, it reviews works adopting Deep Neural Networks (DNNs) for breast thermogram classification.

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Section: Breast Thermographymentioning

confidence: 95%

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

Tsietso

Yahya

Samikannu

2022

Mobile Information Systems

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“…A finite element model involving a realistic breast geometry based on 3D scanning of a mannequin was defined by Mukhmetov et al [78] to improve the accuracy and reliability of computer-aided diagnosis. To experimentally validate their model, the authors created a silicon rubber breast using 3D printing and molding techniques.…”

Section: Forward Modelingmentioning

confidence: 99%

A Review of Techniques and Bio-Heat Transfer Models Supporting Infrared Thermal Imaging for Diagnosis of Malignancy

D’Alessandro,

Tavakolian,

Sfarra

2024

Applied Sciences

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The present review aims to analyze the application of infrared thermal imaging, aided by bio-heat models, as a tool for the diagnosis of skin and breast cancers. The state of the art of the related technical procedures, bio-heat transfer modeling, and thermogram post-processing methods is comprehensively reviewed. Once the thermal signatures of different malignant diseases are described, the updated thermographic techniques (steady-state and dynamic) used for cancer diagnosis are discussed in detail, along with the recommended best practices to ensure the most significant thermal contrast observable between the cancerous and healthy tissues. Regarding the dynamic techniques, particular emphasis is placed on innovative methods, such as lock-in thermography, thermal wave imaging, and rotational breast thermography. Forward and inverse modeling techniques for the bio-heat transfer in skin and breast tissues, supporting the thermographic examination and providing accurate data for training artificial intelligence (AI) algorithms, are reported with a special focus on real breast geometry-based 3D models. In terms of inverse techniques, different data processing algorithms to retrieve thermophysical parameters and growth features of tumor lesions are mentioned. Post-processing of infrared images is also described, citing both conventional processing procedures and applications of AI algorithms for tumor detection.

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“…Without missing any critical data, the PCA method for dimension reduction finds patterns and correlations in the datasets, allowing for a decrease in dimensionality. 38,39 While a dataset with more features takes longer to train the machine learning model, it also increases the amount of data processing involved. Consequently, the joint application of GLCM and PCA improves the classifier's effectiveness, streamlines the analysis procedure, and maintains crucial image properties.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Breast cancer diagnosis through an optimization‐driven multispectral gamma correction (ODMGC)

Ahmad,

2024

Adaptive Control & Signal

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SummaryThe Optimization‐Driven Multispectral Gamma Correction (ODMGC) algorithm overcomes challenges in gathering subtle information and detecting cancer in dense breast thermograms. This algorithm enhances the accuracy of true positives and true negatives while minimising false negatives and false positives. The ODMGC involves a multi‐step optimisation process that categorises grey‐scale images of breast thermograms based on mean brightness. Then, based on the grey levels of the pixels, we grouped each categorisation into sub‐regions. Followed by each group has undergone individually optimised base enhancement. This process enhances the contrast between cancerous and normal tissues, eliminates over‐ and under‐enhancement, and supports breast tumour diagnosis. The optimised‐based enhancement images serve as a reference point for the histogram specification of the V component of the thermograms in the HSV (Hue, Saturation, and Value) model. Further, we evaluated the proposed model using both qualitative and quantitative measures. Finally, using dimension‐reduced significant Grey‐Level Co‐occurrence Matrix (GLCM) features, we validated the results with a Random Forest (RF) classifier. The algorithm was successfully implemented in MATLAB 2020a, and the classifier was developed in Jupyter Notebook using Python. The subjective comparison confirmed the proposed method's superior resolution in normal and malignant cases. The classifier results showed an accuracy of 96.4%, sensitivity of 98.1%, and specificity of 96.9%.

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Thermal modeling for breast tumor detection using thermography

Cited by 14 publications

References 24 publications

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

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

A Review of Techniques and Bio-Heat Transfer Models Supporting Infrared Thermal Imaging for Diagnosis of Malignancy

Breast cancer diagnosis through an optimization‐driven multispectral gamma correction (ODMGC)

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