Smart screening system for breast cancer: the use of thermographic images, evolutionary algorithms, and oversampling

Purpose.This study aimed to develop a computer system for automatic detection of thermographic changes indicating breast malignancy risk.Materials and methods. The database contained 233 thermograms of women, including 43 with malignant lesions and 190 with no malignant lesions. Five classifiers were evaluated (k-Nearest Neighbor, Support Vector Machine, Decision Tree, Discriminant Analysis, and Naive Bayes) in combination with oversampling techniques. An attribute selection approach using genetic algorithms was considered. Performance was assessed using accuracy, sensitivity, specificity, AUC, and Kappa statistics.Results. Support vector machines combined with attribute selection by genetic algorithm and ASUWO oversampling obtained the best performance. Attributes were reduced by 41.38%, and accuracy was 95.23%, sensitivity was 93.65%, and specificity was 96.81%. The Kappa index was 0.90, and AUC was 0.99.Conclusion. The feature selection process lowered computational costs and improved diagnostic accuracy. A high-performance system using a new breast imaging modality could positively aid breast cancer screening.