Breast cancer diagnosis using thermal image analysis: A data-driven approach based on swarm intelligence and supervised learning for optimized feature selection

Breast cancer is one of the deadliest forms of cancer in women but the disease has a good prognosis when diagnosed early. The gold standard for the diagnosis of breast cancer is mammography imaging analysis but the acquisition of mammograms is a painful and embarrassing procedure for women involving breast compression. Alternative methods have been investigated in the last years, including breast thermography, which does not involve ionizing radiation, pain or contact with the breast. However, the accuracy of these modern techniques still needs to be improved to allow the widespread use in practical applications but machine learning techniques have brought in an increased accuracy and reduction in false positives and false negatives to the analysis of breast thermograms. We propose a methodology for detecting and classifying breast lesions using a database of real images of Brazilian patients. We divide our methodology into three steps. In the first step, the shape and texture characteristics of breast thermograms are extracted using Zernike and Haralick moments. The second step is the feature selection process using multi-objective binary optimization algorithms based on swarm intelligence. The third step is to analyze the best vectors’ classification using eleven algorithms such as Convolutional Neural Networks, Extreme Learning Machines, and Support Vector Machines. Finally, we discuss the computational time and performance of various techniques based on swarm intelligence, artificial neural networks, and statistical models to improve the computational time and accuracy of breast cancer diagnoses. Indeed, we observe that the feature selection process has helped us decrease computational time with a high potential to improve diagnostic accuracy. We also demonstrate that the extracted features considering the shape of breast lesions are highly important to a high diagnostic accuracy. •A painless detection of breast lesions helps to prevent Breast Cancer deterioration in women.•The extraction of features from Breast Thermography images can provide enough information for Breast Cancer detection.•Feature selection process using multi-objective algorithms based on swarm intelligence can improve classification tasks.•Multi-Objective Binary Fish School Search (MOBFSS) is efficient on Feature Selection for a real complex problem.•Shape and texture are two types of moments important to the Breast Cancer diagnosis.