## 4 Discussion  
DBT is a three-dimensional imaging modality that can reduce false negatives and false positives in tumour detection caused by overlapping breast tissue in traditional two-dimensional (2D) mammography. Clinical studies suggest CAD models can enhance the identification of breast cancer and aid radiologists in the decision-making process, increasing diagnostic accuracy. In this regard, a multi-class diagnosis system is proposed where a modified deep learning architecture is integrated with data augmentation and colour mapping techniques.  
In this study, scans from the BCS-DBT dataset were augmented with different augmentation techniques to enhance the performance of the DL models by creating newly formed examples for the training set. Moreover, the DBT slices were enhanced with contrast and edge enhancement techniques in the pre-processing stage. Following the pre-processing, a colour mapping technique was applied to remove background artefacts and properly segment the breasts from the background. The features are then extracted using six different CNN. Finally, for each CNN, SVM is deployed to classify the images into three categories:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**DL Models** & **Batch Size** & \
\begin{tabular}{c} **Training** \\ **Accuracy** \\ \end{tabular} &
\begin{tabular}{c} **Testing** \\ **Accuracy** \\ \end{tabular} & **Training Loss** \\ \hline \multirow{3}{*}{**AlexNet**} & **32** & 87.21\% & 86.26\% & 0.72 \\  & **64** & 87.20\% & 89.13\% & 0.69 \\  & **512** & 86.69\% & 90.26\% & 0.50 \\ \hline \multirow{3}{*}{**Mod\_AlexNet**} & **32** & 93.18\% & 87.28\% & 0.25 \\  & **64** & 93.49\% & 90.32\% & 0.25 \\  & **512** & 84.17\% & 90.60\% & 0.52 \\ \hline \hline \end{tabular}
\end{table}
Table 7: RMSProp optimizer performance (3 epochs and batch sizes: 32, 64, and 512).  
normal, benign, and malignant. The three datasets were derived from the BCS-DBT dataset, which comprises information on 5060 patients. As shown by the experiments, the three datasets included scans from 200, 300, and 600 patients, respectively.  
As shown by the experiments, AlexNet outperformed the five DL on all three datasets in terms of accuracy and runtime and proved to be a high-performing DL model in this imaging modality.  
Furthermore, in this study, a new deep learning model (Mod_AlexNet) was developed to improve the performance of AlexNet. The traditional AlexNet now has six more layers, four of which are batch normalisation layers and two of which are maximum pooling layers.  
AlexNet and Mod_AlexNet were tested against the BCS-DBT dataset with 600 patients in this study. Transfer learning was used on AlexNet and Mod_AlexNet, and each model was trained using a different optimization technique, such as SGDM, Adam, and RMSProp, each with batch sizes of 32, 64, and 512.  
Moreover, different performance indicators were measured when comparing the performance of traditional AlexNet and the modified AlexNet (Mod_AlexNet) on Dataset 3 using different optimization techniques and batch sizes. AlexNet's and Mod_AlexNet's performance and accuracy were measured and compared using the metrics of training accuracy, testing accuracy, and training loss. Mod_AlexNet architecture outperformed traditional AlexNet in terms of test accuracy with improvement rates of 3.23%, 1.79% and 1.34% when compared using SGDM, Adam, and RMSProp optimizers, respectively. Mod_AlexNet achieved the highest test accuracy of 91.61% with a training accuracy of 97.67%, and a training loss value of 0.08 optimized by SGDM with a batch size of 32.