A Computational Method for Breast Abnormality Detection Using Thermographs

Breast cancer is the second most common cancer in the world. Early diagnosis and treatment increase the patient's chances of healing. The temperature of cancerous tissues is generally higher than that of healthy neighbouring tissues, making thermography an option to be considered in screening strategies for this type of cancer. In this paper, we propose a computational method for breast Dynamic Infrared Thermography images analysis for screening patients with abnormalities in the breast, using supervised and unsupervised machine learning techniques. An abnormality may be a benign tumor or a malignant tumor (cancer). As performance measure, we use the area under ROC curve, sensitivity, specificity and accuracy. The best results are achieved by K-Star classifier, obtaining an accuracy equal to 98.57%. The results confirm the potential of the proposed method for screening patients with abnormalities in the breast.