An Advanced Ontology based Deep Learning for Computer-aided Interpretation of Mammography Images

Abstract

Computer assisted detection (CAD) are able to detect and characterize suspicious mammographic images, micro calcifications, masses or more difficult, architectural distortion. With the exploitation of these different characteristics, the system can specify and predict the severity of the tumor to assess the risk in terms of Malignity/Benignness. Our work involves the development of a new method for screening breast cancer, this is achieved by developing a whole strategy of knowledge extraction through deep learning and medical ontology appropriate for the classification of regions selected from digital mammograms, for each radiological sign considered, namely, masses and micro calcifications. First, we extracted the parameters characterizing the images used as input to a deep convolutional neuron network CNN. The learning is supervised because the images used are images from the MARATHON database of the University of Florida; they are already diagnosed by experts. The second phase aims to add a semantic level to our classification through a specialized ontology developed for this purpose based on the BIRADS characterization system. Based on the evaluation performed, the proposed approach provides better classification results than the usual methods for assisting in the computer aided detection of breast cancer.