Computer Aided Diagnosis for Breast Diseases Based on Infrared Images

Abstract

In this study, we propose a system to classify thermal images of the breast considering the presence or absence of disease not only tumor and cancer, as most of previous works did. The proposal, at present stage, separates a breast among presenting benign tumor, cancer or being normal and shows how new diagnostic can be included in the data base when a number of proved case of a disease is known. The methodology begins by pre-processing images to be free of those regions that are not of interest for the work before execution of an algorithm for textural features computation, then a set of characteristics is calculated and they are ranged by machine learning classifier, that identity the clinical condition of the patient's breasts. For this classification, a Support Vector Machine (SVM) was used and trained based on already classified diagnosis from two data bases. Tests performed present accuracy above 90%, showing that the proposal is very promising. The project is in the phase of final adjustments and critical use by the medical community for the diagnostic aid. In future work integrations with other examinations is the aim.