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Article

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

1
Federal Institute of Piauí, Teresina 64000-040, Brazil
2
Institute of Computing, Fluminense Federal University, Niterói, Rio de Janeiro 24220-900, Brazil
*
Author to whom correspondence should be addressed.
This paper is an extended version of the paper “Using Series of Infrared Data and SVM for Breast Normality Evaluation” written by A. S. Araujo, T. A. E. da Silva, M. B. H. Moran, A. Conci, published in 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA), Abu Dhabi, United Arab Emirates, 3–7 November 2019.
Sensors 2020, 20(14), 3866; https://doi.org/10.3390/s20143866
Received: 21 May 2020 / Revised: 24 June 2020 / Accepted: 6 July 2020 / Published: 10 July 2020
(This article belongs to the Special Issue Biomedical Infrared Imaging: From Sensors to Applications Ⅱ)
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. View Full-Text
Keywords: breast disease; dynamic infrared thermography; cancer screening; tumor diagnosis breast disease; dynamic infrared thermography; cancer screening; tumor diagnosis
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MDPI and ACS Style

Silva, T.A.E.d.; Silva, L.F.d.; Muchaluat-Saade, D.C.; Conci, A. A Computational Method to Assist the Diagnosis of Breast Disease Using Dynamic Thermography. Sensors 2020, 20, 3866. https://doi.org/10.3390/s20143866

AMA Style

Silva TAEd, Silva LFd, Muchaluat-Saade DC, Conci A. A Computational Method to Assist the Diagnosis of Breast Disease Using Dynamic Thermography. Sensors. 2020; 20(14):3866. https://doi.org/10.3390/s20143866

Chicago/Turabian Style

Silva, Thiago Alves Elias da, Lincoln Faria da Silva, Débora Christina Muchaluat-Saade, and Aura Conci. 2020. "A Computational Method to Assist the Diagnosis of Breast Disease Using Dynamic Thermography" Sensors 20, no. 14: 3866. https://doi.org/10.3390/s20143866

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