Breast cancer is one of the most dangerous diseases for women. Although mammographies are the most common method for its early detection, thermographies have been used to detect the temperature of young women using infrared cameras to analyze breast cancer. The temperature of the region that contains a tumor is warmer than the normal tissue, and this difference of temperature can be easily detected by infrared cameras. This paper proposes a new method to model the evolution of the temperatures of women breasts using texture features and a learning to rank method. It produces a descriptive and compact representation of a sequence of infrared images acquired during different time intervals of a thermography protocol, which is then used to discriminate between healthy and cancerous cases. The proposed method achieves good classification results and outperforms the state of the art ones.
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