## 5 Conclusions  
This paper introduces a new computer-aided multi-class diagnosis system with a modified deep learning architecture (Mod_AlexNet) that effectively classifies DBT scans. The proposed model incorporates augmentation and colour map segmentation techniques with a modified deep learning framework (Mod_AlexNet) for efficient diagnosis. An optimization layer with multiple high performing optimizers is appended to the proposed modified deep learning architecture (Mod_AlexNet) so that it can be evaluated and optimised using various optimization techniques. Investigations were carried out on a large breast tomography benchmark dataset. The proposed multi-class diagnosis system demonstrates the importance of incorporating DBT augmentation and colour feature map segmentation with an optimized deep learning architecture in terms of enhancing performance indicators. To efficiently classify DBT slices, two experimental scenarios are deployed. The first scenario proposed a computer-aided multi-class diagnosis (CAD) model that integrated DBT augmentation, image enhancement techniques, and colour feature mapping with six deep learning models for feature extraction. The second scenario used several optimization techniques with different batch sizes to compare the performance of the newly proposed Mod_AlexNet architecture and traditional AlexNet, and different evaluation performance metrics were computed. In the first scenario, AlexNet improved by 1.69%, 5.13%, 6.13%, 4.79%, and 1.6% when compared to ResNet-18, MobileNetV2, GoogleNet, DenseNet-201, and VGG16, respectively. While for the second scenario, when compared using the SGDM, Adam, and RMSProp optimizers, the proposed Mod_AlexNet architecture outperformed AlexNet in terms of test accuracy with an average improvement rate of 3.23%, 1.79% and 1.34%, respectively. Furthermore, because no underlying specific assertions were made that would inhibit generalisation, the proposed multi-class CAD system is a general framework that can be trained and applied to similar problems and imaging modalities. In the future, the proposed deep learning architecture (Mod_AlexNet) will be modified, and data from more patients will be used to support and enhance the deep learning model's prediction. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12115736/s1](https://www.mdpi.com/article/10.3390/app12115736/s1)]([https://www.mdpi.com/article/10.3390/app12115736/s1](https://www.mdpi.com/article/10.3390/app12115736/s1)). Table S1. ResNet-18's architectural details. Table S2. AlexNet's architectural details. Table S3. GoogleNet layers. Table S4. MobileNetV2 layers. Table S5. Comparison between the six DL models on Dataset 1 after pre-processing in terms of Accuracy with a 95% Confidence Interval (CI) and Run-time in seconds. Table S6. Comparison between the six DL models on Dataset 2 in terms of Accuracy with a 95% Confidence Interval (CI) and Run-time in seconds. Table S7. Comparison between the six DL models on Dataset 1 and Dataset 2 in terms of Accuracy with a 95% Confidence Inter-val (CI) and Run-time in seconds. Table S8. Comparison between the six DL models on Dataset 2 and Dataset 3 in terms of Accuracy with a 95% Confidence Inter-val (CI) and Run-time in seconds. Figure S1. For SGDM optimizer (a) Training Accuracy on Batch size: 32, (b) Training Loss on Batch size: 32, (c) Training Accuracy on Batch size: 64, (d) Training Loss on Batch size: 64, (e) Training Accuracy on Batch size: 512, (f) Training Loss on Batch size: 512. Figure S2. For Adam Optimizer (a) Training Accuracy on Batch size: 32, (b) Training Loss on Batch size: 32, (c) Training Accuracy on Batch size: 64, (e) Training Loss on Batch size: 512. Figure S3. For RMSProp (a) Training Accuracy on Batch size: 32, (b) Training Loss on Batch size: 32, (c) Training Accuracy on Batch size: 64, (d) Training Loss on Batch size: 64, (e) Training Ac-curacy on Batch size: 512, (f) Training Loss on Batch size: 512.