# Classification of Breast Cancer in Mammograms with Deep Learning Adding a Fifth Class
## 5 Discussion  
The implementation of deep Learning in medical imaging brings the evolution of inspection paradigms, making these techniques the new forms of analysis to better detect lesions. As mentioned in [9; 10; 11; 12], human inspection involves a series of errors or omissions in the accurate detection that could lead to fatal events, unnecessary follow-up, or higher costs. As can be seen in this work, all topologies exceed the radiologist's performance (\(\approx 82\%\)). Therefore, the implementation of CAD systems is a must in the early detection of breast cancer. In addition to this, a CAD system does not experience fatigue, is faster, and improves with more training data. We have analyzed the AlexNet, GoogLeNet, Resnet50, and Vgg19 topologies on breast cancer lesions evaluating their AUC, F1 score, accuracy, precision, sensitivity, and specificity, obtaining magnificent performance metrics, especially for GoogLeNet when training with the fifth category of the normal tissue. This work shows that all topologies have better performance when adding another class, confirming our hypothesis by reducing the detection of false positives and false negatives, increasing sensitivity and specificity. The confusion matrix shows that the classifier has relatively lower efficiencies when classifying benign microcalcifications and masses, leading to classifications with false-positive cases. On the other hand, it has greater efficiencies when detecting malign microcalcifications and masses. The misdetections exhibit a soft spot in the classifier; it could confuse benign microcalcifications as malignant and benign masses as malignant but not in the opposite case. As such, the correct detection and classification of the lesions that can occur in a breast is not an easy task [20; 31]. False negatives occur in grayish mammograms associated with dense breast tissue, which could be reduced by implementing CLAHE image enhancement as it dramatically improves areas with low contrast. In this study, we do not use other CNN architectures due to the relationship between the relative prediction time and accuracy. The MobileNet and DarkNet architectures have less accuracy and use a longer prediction time than the architectures tested in this work [40]. Regarding networks, Vgg19 is the one with a more significant number of parameters (144 millions), followed by AlexNet (61 millions) that has more than double of ResNet50 (25.6 millions), and eight times more than GoogLeNet (7 million), which indicates that a vast number of parameters do not generate a certainty of efficiency. Even if we look at the depth of each CNN, AlexNet is the shallowest with 16 layers but performs better than ResNet50, which is the deepest with 50 layers, effectively showing that the overfitting is more significant in the ResNet model. Figure 5: ROC and AUC of the GoogLeNet network trained with five categories and a balanced database. The data we obtained on the analyzed architectures agree with [17; 19] concerning GoogLeNet and AlexNet since similar metrics were acquired. However, these metrics differ from the work of [21], where their AlexNet-based network achieves accuracies above 97% with the Adam optimizer. [33] reported similar performance metrics similar to ours; however, for ResNet50, their values are higher than we achieved. Their shallow DCNN with 100% of all metrics is questioned since they only trained with 1030 ultrasound images to classify benign or malignant masses and normal tissue. The DCNN could incur overfitting; since no data augmentation evidence was found, it would be interesting to test theirs with FEDM images. Moreover, our hypothesis about adding a fifth class confirms the assumption of [18], where they mentioned that adding one more class can improve detection. In [33] and [38] the AUC of Vgg was reported with 94.3% and 93.3%, metrics that are under our approach with five classes, which remarks the aim and extra class.