Feature selection based on dialectics to support breast cancer diagnosis using thermographic images

Purpose Breast cancer is one of the most prevalent types of cancer and the deadliest form of cancer among women. The detection and early diagnosis of cancer are of fundamental importance to increase the possibility of treatment effectiveness, reducing mortality rates. Breast thermography produces high-resolution infrared images that show metabolic changes resulting from the appearance of altered cells in breast tissue. Despite being a promising technique, the interpretation of thermography images is often difficult. Pattern recognition techniques have the potential to work around this problem, helping to extract more useful information from these images. Method In this work, we propose the selection of attributes based on the dialectic method of optimization in breast thermography, aiming to simplify the classifiers and increase the potential of generalization to support the diagnosis of breast lesions. Result Through the proposed attribute selection technique, it was possible to simplify the classifier architectures, reducing the dimensionality of the attribute vectors by about 50% with a low impact on the classification’s correct rates, with a reduction of around 3.72%. Conclusion The proposed method is a promising technique for reducing attributes, with significant accuracy values being obtained using only 84 of the 168 attributes originally extracted. This shows the importance of this step for the use of breast thermography as an auxiliary technique for the diagnosis of breast cancer.