Image enhancement for detection of early breast carcinoma by external irradiation

Arabi, Punal M.; Muttan, S.; Suji, R. Jenkin

doi:10.1109/icccnt.2010.5592580

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…Repeated mammography screening may increase breast cancer risk due to exposure to ionizing radiation [30]. A previous study indicated that each exposure to X-rays increases breast cancer risk by 2% [31].…”

Section: Standard Screening Methodsmentioning

confidence: 99%

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: Standard Screening Methodsmentioning

confidence: 99%

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

et al. 2020

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“…However, this examination is not so efficient for dense breasts and presents substantial false positive rates [ 4 ]. Moreover, this acquisition could cause discomfort to the patient and uses ionizing radiation (X-Rays), increasing by up to 2% the patient’s chance of developing cancer [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Combining Genetic Algorithms and SVM for Breast Cancer Diagnosis Using Infrared Thermography

Resmini

Silva

Araujo

et al. 2021

Sensors

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Breast cancer is one of the leading causes of mortality globally, but early diagnosis and treatment can increase the cancer survival rate. In this context, thermography is a suitable approach to help early diagnosis due to the temperature difference between cancerous tissues and healthy neighboring tissues. This work proposes an ensemble method for selecting models and features by combining a Genetic Algorithm (GA) and the Support Vector Machine (SVM) classifier to diagnose breast cancer. Our evaluation demonstrates that the approach presents a significant contribution to the early diagnosis of breast cancer, presenting results with 94.79% Area Under the Receiver Operating Characteristic Curve and 97.18% of Accuracy.

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“…However, even mammography has some limitations, such as high false positive rates, insufficient effectiveness in dense breasts and use of ionizing radiation. According to Arabi et al [ 3 ], the risk of patient developing breast cancer increases 2% for each X-ray exposure. Moreover, for a better prognosis, minimizing the aggressiveness of the treatment and a decrease in mortality, early diagnosis assumes a decisive role.…”

Section: Introductionmentioning

confidence: 99%

A Computational Method to Assist the Diagnosis of Breast Disease Using Dynamic Thermography

Silva

Muchaluat-Saade

et al. 2020

Sensors

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Breast cancer has been the second leading cause of cancer death among women. New techniques to enhance early diagnosis are very important to improve cure rates. This paper proposes and evaluates an image analysis method to automatically detect patients with breast benign and malignant changes (tumors). Such method explores the difference of Dynamic Infrared Thermography (DIT) patterns observed in patients’ skin. After obtaining the sequential DIT images of each patient, their temperature arrays are computed and new images in gray scale are generated. Then the regions of interest (ROIs) of those images are segmented and, from them, arrays of the ROI temperature are computed. Features are extracted from the arrays, such as the ones based on statistical, clustering, histogram comparison, fractal geometry, diversity indices and spatial statistics. Time series that are broken down into subsets of different cardinalities are generated from such features. Automatic feature selection methods are applied and used in the Support Vector Machine (SVM) classifier. In our tests, using a dataset of 68 images, 100% accuracy was achieved.

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Image enhancement for detection of early breast carcinoma by external irradiation

Cited by 6 publications

References 2 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

Combining Genetic Algorithms and SVM for Breast Cancer Diagnosis Using Infrared Thermography

A Computational Method to Assist the Diagnosis of Breast Disease Using Dynamic Thermography

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