Early diagnosis of mamarian diseases using thermal imaging and machine learning (Revista Brasileira de Contabilidade e Gestão, 2012)
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.
Keywords: thermography; breast cancer; computed aided diagnosis