Framework for automatic segmentation of breast cancer using lightweight convolutional neural networks
Kleber S. Pires, Francisco de Assis Zampirolli · 2021
Given a public database of medical images, testing and comparing different neural network algorithms using that database without a region of interest is a challenging task. This work aims to use the CBIS-DDSM (Curated Breast Imaging Subset of the DDSM - Database for Screening Mammography) and InBreast database, to test a lightweight model of convolutional neural networks (CNN) to segment the masses of tumors. The proposed model is a reduced version of the “U-Net” architecture to be lighter and faster to segment images in computers of routine use of health professionals. The proposed method, available in Google Colaboratory format for easy replication and modifications, can achieve competitive results comparing to the default “U-Net”. This work is a reproducible tool and does not achieve state of art results that uses other methods, but can be customized to enhance accuracy. Results showed that the model can predict tumors masks of both Medial Lateral Oblique (MLO) and Craniocaudal (CC) cases. We define a premise using region of interest to define what is a true positive and with that premise the model achieved a mean dice coefficient of 0.60 and a mean accuracy of 0.40. With test CPU hardware the model can predict 32 images per second, with dedicated GPU the model can predict 237 images per second.