Early detection of the breast cancer using infrared technology – A comprehensive review

Mashekova, A. S.; Zhao, Yong; Ng, Eddie Yin-Kwee; Zarikas, Vasilios; Fok, S.C.; Mukhmetov, Olzhas

doi:10.1016/j.tsep.2021.101142

Cited by 61 publications

(23 citation statements)

References 105 publications

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“…The "SIMULATION" sample quality is ensured by using realistic models of the internal structure of the breast, which contains all the main 3D components of this complex biotissue. Transition from the commonly used multilayer breast model [65,[72][73][74]109] to our multicomponent 3D internal structure seems to be a necessary step to build a better "SIMULATION" sample. Multilayer models give formally higher values of sensitivity and specificity [110], because the tumor as a hot zone stands out very well against the background of an almost uniform temperature distribution.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…We are trying to construct a realistic internal structure of the breast in numerical models of the dynamics of thermal and radiation fields (See Subsections 2.2 and 2.3 below). This distinguishes our approach from traditional multilayer biotissue models [92,106,107,109], which use a sequential set of homogeneous layers.…”

Section: Methods For 3d Reconstruction Of Multicomponent Tissuementioning

confidence: 99%

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Improving the Efficiency of Oncological Diagnosis of the Breast Based on the Combined Use of Simulation Modeling and Artificial Intelligence Algorithms

Хоперсков

Polyakov

2022

Algorithms

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This work includes a brief overview of the applications of the powerful and easy-to-perform method of microwave radiometry (MWR) for the diagnosis of various diseases. The main goal of this paper is to develop a method for diagnosing breast oncology based on machine learning algorithms using thermometric data, both real medical measurements and simulation results of MWR examinations. The dataset includes distributions of deep and skin temperatures calculated in numerical models of the dynamics of thermal and radiation fields inside biological tissue. The constructed combined dataset allows us to explore the limits of applicability of the MWR method for detecting weak tumors. We use convolutional neural networks and classic machine learning algorithms (k-nearest neighbors, naive Bayes classifier, support vector machine) to classify data. The construction of Kohonen self-organizing maps to explore the structure of our combined dataset demonstrated differences between the temperatures of patients with positive and negative diagnoses. Our analysis shows that the MWR can detect tumors with a radius of up to 0.5 cm if they are at the stage of rapid growth, when the tumor volume doubling occurs in approximately 100 days or less. The use of convolutional neural networks for MWR provides both high sensitivity (sens=0.86) and specificity (spec=0.82), which is an advantage over other methods for diagnosing breast cancer. A new modified scheme for medical measurements of IR temperature and brightness temperature is proposed for a larger number of points in the breast compared to the classical scheme. This approach can increase the effectiveness and sensitivity of diagnostics by several percent.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Methods For 3d Reconstruction Of Multicomponent Tissuementioning

confidence: 99%

Improving the Efficiency of Oncological Diagnosis of the Breast Based on the Combined Use of Simulation Modeling and Artificial Intelligence Algorithms

Хоперсков

Polyakov

2022

Algorithms

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“…As earlier described in the methodology, the convolutional neural network (CNN) deep learning and artificial intelligent algorithm for image classification was employed to analyse and classify a few datasets (a total of 20 breast cancer mammographic images, where 10 were for benign and 10 for malignant classes, respectively). The maximum pixel size of the pooling layer is also (2,2) and the pooling stride is also 2. By using the ReLU activation function, which is the most used activation function in the world right now; results were first obtained with one convolution and one pooling layer system, and the results shown in Figure 14.…”

Section: Breast Cancer Mammography Image Classification Using Cnn And...mentioning

confidence: 99%

“…Between 2016 to 2020 alone, official reports from the World Health Organisation(WHO) omdicates aapproximately 7.8 million cases of breast cancer, of which 685,000 of them have died, which makes it a serious killer of an ailment [1]. For easier treatment of breast cancer and also the accompanying reduction of the mortality rate caused by the disease; early detection is pertinent and must be continually optimized to fight the disease effectively and efficiently [2]. It is therefore recommended for women aged between 40 to 75 years old have periodic breast screening, because early breast diagnosis and treatment of breast lesions, as shown by facts, can be achieved by breast screening performed by using different X-ray imaging techniques [3].…”

Section: Introductionmentioning

confidence: 99%

Analytical Optimization of X-ray Mammography for Increased Benefits and Safety; using: Data Analytics, Electronics Engineering and Artificial Intelligence

Kalu¹

2023

J Health Statist Rep

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Objective: To analyse, understand and investigate common X-ray Mammography techniques, terminologies and properties; and how the X-ray Mammography systems can be optimized for a safer, more effective, more efficient, more energy efficient procedure for breast cancer diagnosis; using Deep Learning (DL)/ Artificial Intelligence (AI), Analytical, Electronic Engineering, tools and Methodologies. Methods: Using data and breast cancer image datasets from validated open source data stores; investigative and comparative analyses were carried out on common X-ray Mammography techniques in relation to breast cancer diagnosis and treatments for Women; as well as optimization analyses using Electronic engineering principles and Artificial Intelligence principles on the quality, intensity, radiation photon energy, HVL(Half Value Layer), Tube current-time product, Metal filters properties (e.g. K-edge energy cut off), and classification validation accuracy of the X-ray Mammography properties, procedure, processes and systems. The methodical and data-driven analyses were carried out using the following Data, Artificial Intelligence (AI) and Electronic Engineering, methodologies and algorithms: Data Analytics, Convolutional Neural Networks (CNN) in Machine Learning (ML) Engineering, and X-ray-Impedance Circuit Composite System Analysis

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“…Mammography also exposes women to higher levels of radiation and is less effective in younger women [4] [5]. Thermography, in contrast, offers a practical non-invasive breast cancer screening option to mammography that is nonionizing, non-contact, and inexpensive [5]. Modern thermography-based analysis methods, according to research, are capable of accurately diagnosing breast cancer [6].…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Analytics Classification using MEFBUD DCNN Techniques

Priyadharsini .M,

Sathiaseelan

2023

IJRITCC

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Breast cancer is the most dangerous and deadly form of cancer. Initial detection of breast cancer can significantly improve treatment effectiveness. The second most common cancer among Indian women in rural areas. Early detection of symptoms and signs is the most important technique to effectively treat breast cancer, as it enhances the odds of receiving an earlier, more specialist care. As a result, it has the possible to significantly improve survival odds by delaying or entirely eliminating cancer. Mammography is a high-resolution radiography technique that is an important factor in avoiding and diagnosing cancer at an early stage. There are numerous procedures and approaches for detecting cancer in the tissues of the breast. This work presents the image processing, segmentation, and deep learning methodologies and approaches for the diagnosis of breast cancer. This research will help people make better decisions and use trustworthy techniques to find breast cancer early enough to save a woman's life. Pre-processing, segmentation, and classification are some of this system's steps. We've included a thorough study of several techniques or processes, along with information on how they're used and how performance is measured. The stated results lead to the conclusion that, in order to increase the chances of surviving breast cancer, it is crucial to develop new procedures or techniques for early diagnosis. For researchers to effectively diagnose breast cancer, segmentation and classification phases are also difficult. Therefore, the precise diagnosis and categorization of breast cancer still require the use of more advanced equipment and techniques.

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Early detection of the breast cancer using infrared technology – A comprehensive review

Cited by 61 publications

References 105 publications

Improving the Efficiency of Oncological Diagnosis of the Breast Based on the Combined Use of Simulation Modeling and Artificial Intelligence Algorithms

Improving the Efficiency of Oncological Diagnosis of the Breast Based on the Combined Use of Simulation Modeling and Artificial Intelligence Algorithms

Analytical Optimization of X-ray Mammography for Increased Benefits and Safety; using: Data Analytics, Electronics Engineering and Artificial Intelligence

Breast Cancer Analytics Classification using MEFBUD DCNN Techniques

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