## 5 Discussion
### Shape-Based Breast Cancer Classification  
Quantitatively, the extensive experimental results are elaborated, considering the pretrained DL methods on both with and without data augmentation configurations. The mean AUC values of the developed models improved during the augmentation phases. The crown of overall best performing algorithm belongs to DenseNet-161 due to persistent performance, i.e., reaching higher than 96.0% across _No Aug_, _Basic Aug_, and _Adv Aug_ setups.  
In particular, the best-performing model, i.e., DenseNet-161, increases by 1.45% and 1.97% in the mean AUC from _No Aug_ to _Basic Aug_ and _Adv Aug_, respectively. It impressively increases by 33.01%, 33.28%, and 27.10% over the SqueezeNet in configuration-to-configuration comparison, i.e., _No Aug_ to _No Aug_, and so on. In the case of _Basic Aug_, the ResNeXt outperforms all other architectures with a percent increase of 33.56 from the worst-performing model.  
Since the results are comparable, any particular model performing best in terms of AUC and loss may not perform ideally in all aspects. The reason is the primitive learning and weight updating mechanism of the CNN models. For example, in the _No Aug_ phase, three out of four individual AUC values of ResNeXt among classes remain higher than the respective individual AUC values of the DenseNet-161, despite the equal mean AUC.  
The utilization of the augmentation techniques revamped the trends of the validation loss, as shown in Figures 6 and 7; however, the improvement in the validation loss is negative for the worst-performing model, i.e., SqueezeNet, which fluctuates between 0.3 to 1.3 and 0.7 to 1.3 for _No Aug_ and _Adv Aug_ configurations, respectively. Both the training and validation losses increased.  
The illustrated loss values are evidently coherent to the fact that a huge model such as DenseNet-161, with tons of parameters, overfits when it is trained with no augmentation over an increasing number of epochs in our experimental setup. Instead, SqueezeNet has the opposite problem, being unable to even properly comprehend the fundamental patterns, resulting in an underfit behavior. After augmentation, underfitting problem of SqueezeNet cannot be resolved, as shown by comparing Figures 6 and 7, but the overfitting issue of the DenseNet-161 is mitigated as presented by comparing Figures 6a and 7a.  
A noteworthy consideration arises when considering the performance of a model in relation to its size and complexity. In Figure 8, a noteworthy trend exists between the number of parameters and the AUC, the models having a huge number of parameters compromised at the mean AUC at certain levels. On the contrary, the models with an extremely low number of parameters may result in bad generalization performance, since with reduced number of parameters, the model is hardly able to learn simple patterns in our study.  
Nevertheless, the two different augmentation configurations and three different execution setups (i.e., 10, 30, and 50 epochs) disclose a clear improvement with augmentation in our DBT classification framework. The basic augmentation improves performance compared to no augmentation, and the advanced augmentation plays its part and further increases the AUC outcomes. One major reason reckoned is the considerably high visually noticeable resemblance between the training and the validation data, whereas the state-of-the-art architectures, the significant clinical data, and the RoI-level cropped images may possibly be other driving causes.