# Classification of Breast Cancer in Mammograms with Deep Learning Adding a Fifth Class
## 6 Conclusions  
Mammography as an early detection technique has been vital in the fight against breast cancer, and the work carried out by specialists in the diagnosis is critical. Additionally, CAD systems can provide significant support by performing diagnoses with greater accuracy in less time than a human being. For this work, we inspected the performance of three CNN topologies after training them with a balanced and an unbalanced database, using 4 and 5 classes corresponding to benign and malign microcalcifications and masses plus a fifth class of the \"normal\" type to represent the non-lesioned tissue of the breast. Data augmentation played an essential role in this work since out of 539 annotated regions of masses and calcifications in the MIAS and INbreast databases, we reached 12,582 regions. Augmentation was made by rotating the images in 90\({}^{\circ}\) intervals, rotating from left to right, removing its background, and improving its quality with the CLAHE method. The transfer learning and fine-tuning concepts applied in this work produced outstanding results after using the AlexNet, GoogLeNet, ResNet50 and Vgg19 topologies, and adapting their output parameters for training. The AUC of all networks performed above 95%. The GoogLeNet topology obtained the best performance metrics when trained for five classes with a balanced database of 2336 regions for each class; that is, 11,680 regions in total. The images were divided into 8175 for training and 3505 for tests corresponding to 70% and 30%; obtaining an AUC = 99.29%, F1 score = 91.92%, effectiveness = 91.70, and precision = 92.15%, sensitivity = 91.70%, and specificity = 97.66%. These values were the highest recorded except for the effectiveness only surpassed by the same GoogLeNet network trained with five classes in the unbalanced database, obtaining a value of 91.97%; 0.27% more accurate. The CNN GoogLeNet is followed by the Vgg19 CNN trained with five classes in a balanced dataset as well, having a close development in its metrics and even with a better AUC of 99.50%. The training with four classes of the GoogLeNet topology in a balanced database achieved the third place, differing only 0.15% in the AUC; in the same way, it obtains values above 90% in all its metrics. Therefore, we consider that our hypothesis about adding the 5 th class of normal breast tissue improves the classifier's performance, although not to a great extent, but enough to differentiate itself and improve the medical diagnosis. Generally, CNN's trained with five classes performed better than their analogs trained with four classes and this can be noted in a great manner looking at the Vgg19 topologies in Table 4.  
On the other hand, the networks with the lowest performance were those trained with the unbalanced database; in more particular cases, it occurred in the Resnet50 topology, associating their rapid convergence with the low number of training epochs and lower classification efficiency; this can be noted too in the Vgg19 CNN trained with four classes. A CAD system in which these classifiers participate could reduce the rate of false positives findings and eliminate the follow-up associated with these cases and biopsies and appointments or double examinations. In addition, CAD systems could include information from the patients' clinical history or family medical histories to correlate with breast cancer to increase the correct diagnosis made by specialists. Conceptualization, S.C.-T. and L.O.S.-S.; methodology, H.A.G.-O. and S.C.-T.; software, S.C.-T.; validation, S.C.-T., L.O.S.-S. and C.L.C.-M.; formal analysis, S.C.-T. and J.M.O.-R.; investigation, C.L.C.-M., and L.O.S.-S.; resources, L.O.S.-S., J.D.M.-S. and M.d.R.M.-B. ; data curation, S.C.-T. and J.D.M.-S.; writing--original draft preparation, S.C.-T., and H.A.G.-O. ; writing--review and editing, L.O.S.-S., J.M.O.-R., and G.D.-F.; visualization, H.A.G.-O. and J.D.M.-S.; supervision, G.D.-F.; project administration M.d.R.M.-B. and C.A.O.-O.