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Breast cancer is the leading type of cancer among women. According to the World Cancer Research Fund, in 2018, over 2 million new cases were detected around the world. Despite its high occurrence, early detection provides a better prognosis and helps increases the patient’s survival. Significant advances in screening techniques, such as infrared imaging, have provided a cheap and less invasive way to detect the disease. Besides, computational tools can be used to assist doctors in providing a better diagnosis. Therefore, this paper presents a segmentation method based on U-Net Convolutional Neural Networks. In contrast to the state-of-the-art, machine learning approaches have been shown to be efficient for ROI segmentation, reaching an accuracy of 98.24% over the frontal view, and 93.6% over the lateral view. This segmentation method may be very useful for classification tasks, once the region of interest is well delimited for feature extraction.

U-Net Convolutional Neural Networks for breast IR imaging segmentation on frontal and lateral view