Breast cancer is one of the major causes of women death worldwide. WHO organization has reported that 1 in every 12 women could be subjected to a breast abnormality during her lifetime. To increase survival rates, it is found that early detection of breast tumor is very critical. Mammography-based breast cancer screening is the leading technology to achieve this aim. However, it still can not deal with the cases where the tumor size less than 2mm. Thermography-based breast cancer detection methods can address this problem. In this paper, a breast cancer detection method is proposed. The proposed method is consists of four phases: (1) Image Pre-processing using homomorphic filtering, (2) Region of interest (ROI) Segmentation using K-mean clustering, (3) feature extraction using signature boundary, and (4) classification using Extreme Learning Machine (ELM). The proposed method is evaluated using the public dataset DMR-IR. Different activation functions in ELM are evaluated. The obtained results founded that “Tribas” is the best activation function under different experiments. It produced an accuracy result of 95.94% while talking 0.0469 second to detect the existence of malignant tumor, benign tumor or normal image. These promising results would be useful to develop thermography-based breast cancer detection system.
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