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Introduction: Breast cancer is the most common cancer in women and one of the major causes of death from cancer among female around the world. The early detection and treatment are the major way to healing. The use of mammary thermography in Mastology is increasing as a complementary imaging technique to early detect lesions. Its use as a screening exam to identify breast disorders has been investigated. The aim of this study is to investigate the behavior of different classification methods while grouping the thermographic images into specific types of lesions. Methods: To evaluate our proposal, we built classifiers based on artificial neural networks, decision trees, Bayesian classifiers, and Haralick and Zernike attributes. The image database is composed by thermographic images acquired at the University Hospital of the Federal University of Pernambuco. These images are clinically classified into the classes cyst, malignant and benign. Moments of Zernike and Haralick were used as attributes.Results: Extreme Learning Machines (ELM) and Multilayer Perceptron networks (MLP) proved to be quite efficient classifiers for classification of breast lesions in thermographic images. Using 75% of the database for training, the maximum value obtained for accuracy was 73.38%, with a Kappa index of 0.6007. This result indicated to a sensitivity of 78% and specificity of 88%. The overall efficiency of the system was 83%. Conclusion: ELM showed to be a promising classifier to be used in the differentiation of breast lesions in thermographic images, due to its low computational cost and robustness.Keywords Breast cancer early diagnosis, Thermographic images, Mammary thermography, Artificial neural networks, Extreme learning machines.This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.How to cite this article: Santana MA, Pereira JMS, Silva FL, Lima NM, Sousa FN, Arruda GMS, Lima RCF, Silva WWA, Santos WP. Breast cancer diagnosis based on mammary thermography and extreme learning machines. Res Biomed Eng. 2018; 34(1):45-53.

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OBJECTIVES This study sought to evaluate the impact of patient–prosthesis mismatch (PPM) on the risk of perioperative, early-, mid- and long-term mortality rates after surgical aortic valve replacement. METHODS Databases were searched for studies published until March 2018. The main outcomes of interest were perioperative mortality, 1-year mortality, 5-year mortality and 10-year mortality. RESULTS The search yielded 3761 studies for inclusion. Of these, 70 articles were analysed, and their data were extracted. The total number of patients included was 108 182 who underwent surgical aortic valve replacement. The incidence of PPM after surgical aortic valve replacement was 53.7% (58 116 with PPM and 50 066 without PPM). Perioperative mortality [odds ratio (OR) 1.491, 95% confidence interval (CI) 1.302–1.707; P < 0.001], 1-year mortality (OR 1.465, 95% CI 1.277–1.681; P < 0.001), 5-year mortality (OR 1.358, 95% CI 1.218–1.515; P < 0.001) and 10-year mortality (OR 1.534, 95% CI 1.290–1.825; P < 0.001) were increased in patients with PPM. Both severe PPM and moderate PPM were associated with increased risk of perioperative mortality, 1-year mortality, 5-year mortality and 10-year mortality when analysed together and separately, although we observed a higher risk in the group with severe PPM. CONCLUSIONS Moderate/severe PPM increases perioperative, early-, mid- and long-term mortality rates proportionally to its severity. The findings of this study support the implementation of surgical strategies to prevent PPM in order to decrease mortality rates.