Early diagnosis of mamarian diseases using thermal imaging and machine learning

Authors

DOI:

https://doi.org/10.5965/2316419001012012055

Keywords:

thermography, breast cancer, computed aided diagnosis

Abstract

Cancer is a class of diseases characterized by out-of-control cell growth, they have lost their function in tissue and do not die. This reproduction increases the local temperature because new blood vessels, neo-angiogenesis, are promoted by cancer cells. The medical thermography is a way to acquire the skin temperature and analyze these patterns. The human body is almost symmetric considering the sagittalplane that is the plane that divides the body in right and left parts, when there are great changes in the temperature pattern between right and left breast possible pathology must be investigated. This work aims to explore the possibilities of pattern recognition techniques on the classification of the images from the ProENG project as from healthy orpathological mamma. Threedifferent groups of feature are extracted from the thermal images of this project: statistic features, fractal geometry based features and geo-statistic features. Three classifiers have been tested: SVM, KNN and Naïve Bayes. Additionally two feature reduction techniques have been used: PCA and Information Gain Ratio. The results are promising: 90% of accuracy and 0.9 for the area under ROC.

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Author Biographies

Roger Resmini, Fluminense Federal University, UFF, Brazil.

PhD in Computing Sciences from the Fluminense Federal University, UFF, Brazil.

Has a Master’s degree in Computing Sciences from the Fluminense Federal University, UFF, Brazil.

Graduated in Computing Sciences at the Rondonópolis Arnaldo Estevão UNIC, UNIC, Brazil.

Adjunct Professor at the Mato Grosso Federal University, CUR, ICEN.

Aura Conci, Fluminense Federal University, UFF, Brazil.

PhD in Civil Engineering from the Pontifical Catholic University of Rio de Janeiro, PUC-Rio, Brazil.

Has a Master’s degree in Civil Engineering from the Pontifical Catholic University of Rio de Janeiro, PUC-Rio, Brazil.

Graduated in Civil Engineering at the Espírito Santo Federal University, UFES, Brazil.

Professor at the Fluminense Federal University, Computing Sciences Institute.

Tiago Bonini Borchartt, Fluminense Federal University, UFF, Brazil.

PhD in Computing Sciences from the Fluminense Federal University, UFF, Brazil.

Has a Master’s degree in Computing Sciences from the Santa Maria Federal University, UFSM, Brazil.

Graduate in Computing Sciences at the Universidade Federal Fluminense, UFF, Brazil.

Graduated in Computing Sciences at the Franciscan University, UFN, Brazil.

Professor at the Maranhão Federal University, Technological Center, Department of Informatics.

Rita de Cássia Fernandes de Lima, Federal University of Pernambuco, UFPE, Brazil.

PhD in Nuclear Technology from the University of São Paulo, USP, Brazil.

Has a Master’s degree in Nuclear Energy Technologies from the Pernambuco Federal University, UFPE, Brazil.

Graduated in Physics at the Pernambuco Federal University, UFPE, Brazil.

Professor at the Pernambuco Federal University, UFPE, Brazil.

Anselmo Antunes Montenegro, Fluminense Federal University, UFF, Brazil.

PhD in Informatics from the Pontifical Catholic University of Rio de Janeiro, PUC-Rio, Brazil.

Has a Master’s degree in Informatics from the Pontifical Catholic University of Rio de Janeiro, PUC-Rio, Brazil.

Graduated in Computing Sciences at the Fluminense Federal University, UFF, Brazil.

Professor at the Fluminense Federal University, UFF, Brazil.

Cristina Asvolinsque Pantaleão, Fluminense Federal University, UFF, Brazil.

PhD in Medical Sciences from the Fluminense Federal University, UFF, Brazil.

Has a Master’s degree in Medicine (Radiology) from the Rio de Janeiro Federal University, UFRJ, Brazil.

Specialist in Radiology from the Federal Council of Medicine, CFM, Brazil.

Specialist in Radiology from the Brazilian Society of Radiology, SBRad, Brazil.

Specialist in Medical Residency from the Fluminense Federal University, UFF, Brazil.

Graduated in Medicine at the Federal Fluminense University, UFF, Brazil.

Professor at the Fluminense Federal University, UFF, Brazil.

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Published

2012-06-13

How to Cite

Resmini, R., Conci, A., Borchartt, T. B., Lima, R. de C. F. de, Montenegro, A. A., & Pantaleão, C. A. (2012). Early diagnosis of mamarian diseases using thermal imaging and machine learning. Revista Brasileira De Contabilidade E Gestão, 1(1), 55–67. https://doi.org/10.5965/2316419001012012055

Issue

Vol. 1 No. 1 (2012)

Section

Articles

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Copyright (c) 2012 Roger Resmini, Aura Conci, Tiago Bonini Borchartt, Rita de Cássia Fernandes de Lima, Anselmo Antunes Montenegro, Cristina Asvolinsque Pantaleão

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