Novel Feature Selection Using Machine Learning Algorithm for Breast Cancer Screening of Thermography Images

Early diagnosis and treatment are the keys to managing patients with breast cancer. Identifying patients even before they present with symptoms is made possible through screening methods. Thermography is a tool for screening carcinoma breast to reduce the associated morbidity and mortality. This paper proposes a novel feature selection using a machine learning algorithm, namely, the Greedy search optimization algorithm. This algorithm is applied to compare various features selection techniques. These techniques are sequential backward (SBS), sequential forward feature selection, and exhaustive feature selection techniques. It is concluded from this comparison that sequential backward feature selection is the best technique for breast cancer diagnosis. Our average score of SBS comes 88.5714%, with a computational time of 87.4 s. For classification purposes, we have used an artificial neural network. The classification result varies according to the age group with the physiology of the breast. Considering this, we have selected features age-group wise by sequential backward technique. The classification accuracy of (20–40), (41–60), (61–80) years age group patients are 79.349%, 80.711%, and 74.76%.