Segmentation and Classification of Noisy Thermographic Images as an Aid for Identifying Risk Levels of Breast Cancer

Abstract

During 2016, breast cancer was the second cause of death among women within ages of 24–30. This makes mandatory to find reliable strategies that support physicians and medical services for early diagnosis of such disease. Currently, mammography is considered the gold instrument for assessing the risk of having breast cancer, but other methods are being analyzed, looking for systems that may be cheaper and easier to apply, including the use of thermographic images. In this paper, we present an analysis of the performance of a system based on a “Feed-Forward Neural Network” (FFNN), for the identification of two and three levels of risk cancer. Indeed, a system based on a “Regions-Convolutional Neural network” (R-CNN) for automatic segmentation of the breast is proposed. Both systems were tested in a private database developed by the “Center for Studies and Cancer Prevention, A.C.” located in Oaxaca, Mexico, which presents important challenges as class unbalances, a slack recording with respect to application of the protocol and noise. The systems were evaluated using three subsets of the database, built using images with different levels of challenges. Our results showed that a FNNN classifier performed well only with data strictly following the protocol, while the levels of performance with noisy data are not yet acceptable for real applications. In the other hand, the results obtained by the automatic segmentation based on R-CNN were competitive, encouraging for more research in this area.