# Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review
## 4 Discussion and Conclusions
Furthermore, various studies [39; 40; 41; 55] prove that those CNN methods that compare images from CC and MLO views improve the accuracy of detection and reduce the FPR. Furthermore, different evaluation metrics are described in Tables 3 and 4 as corroboration of the performance of these techniques. The results in Tables 6 and 7 describe different research where their authors have used a variety of datasets (Table 1), approaches, and performance metrics to evaluate CNN techniques in DM and US imaging. For example, better results were achieved in DM analysis by [PERSON] [145] with YOLO5 using DDSM data augmentation, while [PERSON] et al. [181] used a deep CNN (Inception V3) with DDSM and MIAS datasets. On the other hand, [PERSON] et al. [49] introduced a DenseNet model to analyze private (BUSI and SNUH) US datasets. [PERSON] et al. [66] achieved high accuracy with the VGG19 deep CNN model using the ImageNet database. Similarly, [PERSON] et al. [152] attained an accuracy of 96.89% with SSD + ZFNet and [PERSON] et al. [62] reached 91.23% using a private dataset with GoogleNet. Likewise, Table 8 contains a literature review for the comparison of the evaluation metrics between DL-CAD systems and traditional ML-CAD systems. Even though Table 8 shows that [PERSON] et al. [183] presented a good traditional wavelet neural network CAD system with high accuracy (93.67%) and AUC of 96.85%, [PERSON] et al. [42] exceeded this percentage using a CNN + SVM DL-CAD system with DDSM (99%) and MIAS (97.18%) DM datasets. In US images [PERSON] et al. [36] and [PERSON] et al. [190] proved that a DL-CAD based on CNN and a deep polynomial network achieved better results in terms of accuracy (93.4 and 92.40%) and AUC (94.7%), respectively. In the same way, DL-CAD reached higher values than ML-CAD when used on private US images. For example, [PERSON] et al. [35] and [PERSON] et al. [41] showed ML based on an ANN for segmentation and classification that reached accuracies of 78.5 and 95.86% and an AUC of 82%, respectively. These works demonstrate that in most cases, the DL architectures outperformed traditional methodologies. To conclude, the use of DL could be a promising new technique to obtain the main features for automatic breast tumor classification, especially in dense breasts. Furthermore, in medical image analysis, using DL has proven to be better for researchers compared to a conventional ML approach [41; 42]. It appears as though DL provides a mechanism to extract features automatically through a self-learning network, thus boosting the classification accuracy. However, there is a continuing need for better architectures, more extensive datasets that overcome class imbalance problems, and better optimization methods. Finally, the main limitation in this work is that several algorithms and results are not available in the open literature because of proprietary intellectual property issues. **Author Contributions:** Conceptualization, Y.J.-G. and V.L. ; methodology, Y.J.-G.; formal analysis, Y.J.-G., M.J.R.-A., and V.L. ; investigation, [PERSON]-G.; resources, [PERSON]-G.; writing--original draft preparation, [PERSON]-G.; writing--review and editing, [PERSON]-G., [PERSON]-A., and V.L. ; visualization, Y.J.-G.; supervision, M.J.R.-A. and V.L. ; project administration, M.J.R.-A. and V.L. ; funding acquisition, V.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This project has been co-financed by the Spanish Government Grant PID2019-107790 RB-C22, \"Software development for a continuous PET crystal systems applied to breast cancer\". **Conflicts of Interest:** The authors declare no conflict of interest.