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Breast cancer is a neoplasm resulting from the multiplication of abnormal cells in the breast tissue that initially form nodules or thickening. The survival of patients with the disease can be highly effective in cases where the diagnosis is early. However, this diagnosis requires the analysis of extensive histopathological breast tissue samples by specialists. This task is considered complex and may require the attention of the pathologist for a long period of time. Fortunately, computational methods, which include digital image processing and deep learning, can help quickly identify tumour malignancies on images from these scans and speed up diagnosis. Thus, this work proposes a methodology for the automatic diagnosis of malignant breast lesions on histopathological images based on the mutual measurement of bioinspired texture features (BiT) and deep learning features. Additionally, in this study, we also evaluated the effect of methods of colour normalisation to reduce colour variation in the images. The proposed methodology obtained a result of 92.9% accuracy with the random forest classifier. These numbers show that a combination of BiT with deep features produces better results than the techniques applied individually, and show that the proposed method can be used to compose computer-aided detection systems.

Malignant breast lesions detection in histopathological images based on the combination of bioinspired texture descriptors and deep features