2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA) 2019

DOI: 10.1109/aiccsa47632.2019.9035222

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Using Series of Infrared Data and SVM for Breast Normality Evaluation

Adriel S. Araujo

¹

,

T. A. E. da Silva

²

,

Maira Beatriz Hernández Morán

³

et al.

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Cited by 5 publications

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“…This model was evaluated and achieved an accuracy of 100% in our tests. This paper is an extended version of the paper described in [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

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

¹

,

²

,

Muchaluat-Saade

³

et al. 2020

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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.

“…This model was evaluated and achieved an accuracy of 100% in our tests. This paper is an extended version of the paper described in [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

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

¹

,

²

,

Muchaluat-Saade

³

et al. 2020

View full text Add to dashboard Cite

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.

A Thermographic Time Series Approach for Evaluating the Breast Cancer Treatment

Araujo,

Issa,

Chagas

et al. 2023

2023 20th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA)

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On using convolutional neural networks to classify periodontal bone destruction in periapical radiographs

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,

²

,

³

et al. 2020

2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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