According to the American Cancer Society [1], breast cancer represents 25 per cent of the new cancer diagnostics in women worldwide, being the greatest cause of death in the world’s developing regions. This study aims to present ways of using Deep Learning (DL) algorithms in the automatic analysis of medical images representing pathological lesions of breast cancer masses. The purpose of this work is to highlight DL functionalities to improve the current machine learning (ML) based breast cancer diagnosis methods (CADx). CADx methods have been...

Background: The first uses of DL methods in CADx systems from breast cancer diagnosis were made by Sahiner et al. in 1996 [2], where a convolutional neural network (CNN) was used to classify mass and normal breast tissue. This approach had the first phase of image preprocessing, where a region of interest (ROI) was selected and a series of operations (e.g. filters) were applied in order to facilitate the learning process by the CNN. This two-stage approach was used by other solutions, varying the preprocessing...

Experimental Setup: The models were trained using a stochastic gradient descent learner. For each run, the model was trained for a maximum number of 150 epochs, using a strategy of early stopping when there are no improvements. This experimental setup allowed us to verify the integrity of our system and find the best combination of classifiers giving us the best performance in classifying breast cancer tumours. To verify the integrity of these models, we carried out a 10-fold cross-validation method for each model. Film Mammography...

When compared with the base approach our new proposed method improves the classifier AUC from 0.826 to 0.978. The best approach uses the DL model for feature representation learning, in this case, the penultimate layer from the DL model, to feed a Random Forest classifier that enables, in average, to correctly classify 97.93% of the test data (BCDR-F03 dataset). The test in FFDM showed surprising results, giving us an accuracy of over 75% in all classifiers. This fact was significant since the existing samples of...

[1] A. C. Society, Global Cancer Facts & Figures. Atlanta: American Cancer Society, third ed., 2015. [2] B. Sahiner, H.-P. Chan, N. Petrick, D. Wei, M. A. Helvie, D. D. Adler, and M. M. Goodsitt, “Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images,” IEEE Transactions on Medical Imaging, vol. 15, pp. 598–610, Oct 1996. [3] P. Fonseca, J. Mendoza, J. Wainer, J. Ferrer, J. Pinto, J. Guerrero, and B. Castaneda, “Automatic breast density classification using...