## 5 Conclusions  
This paper presents a novel DeepBarestCancerNet deep learning model for BC detection and classification. The proposed framework has 24 layers, including six convolutional layers, nine inception modules and one fully connected layer. We observed that the proposed model reached the highest classification accuracy of 99.35%. Furthermore, we compared the performance of several Deep Learning models, and the experiment results showed that our model outperformed the others. Furthermore, we validated the proposed model using another standard, publically available dataset. The proposed model reached  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Work** & **Method** & **Accuracy** \\ \hline [PERSON] et al. [32] & DL + TL & 99.5\% \\ \hline [PERSON] et al. [34] & DL GoogleNet + VGG & 97.52\% \\ \hline [PERSON] et al. [40] & DL + TL & 98.96\% \\ \hline Proposed Method & DCNN & 99.35\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison with state of the art work.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Accu** & **Prec** & **Recall** & **F1-Score** \\ \hline Proposed Model & 99.63\% & 99.50\% & 100\% & 99.50\% \\ AlexNet & 97.40\% & 97\% & 97.30\% & 97.30\% \\ GoogLeNet & 99.26\% & 99.20\% & 98.76\% & 99.50\% \\ Shufflenet & 99.26\% & 99.20\% & 98.70\% & 99.50\% \\ ResNet-50 & 99\% & 99\% & 99\% & 99\% \\ SqueezeNet & 99\% & 99\% & 99\% & 99\% \\ ResNet-18 & 99.50\% & 99.50\% & 99.33\% & 99.30\% \\ Resnet101 & 99\% & 99\% & 98.33\% & 98.33\% \\ ExceptionNet & 99.30\% & 99.30\% & 99\% & 99\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Experimental results on binary BC dataset.  
the highest accuracy of 99.35%. Moreover, this paper utilizes eight pre-train DL for BC detection using the TL technique. We evaluated the performance of nine DL-based models on a standard Dataset consisting of 537 benign, 360 malignant 133 normal ultrasound images. The limited images in the publicly available BC ultrasound imaging dataset, which impact DL models' performance, are a limitation of this research. This research can be further improved by including more images in the dataset. Furthermore, we validated the proposed model using another standard, publically available binary dataset. The proposed model reached the highest accuracy of 99.63%.  
Additionally, as the suggested model extracts increasingly in-depth, accurate, and discriminatory features, testing with the dataset reveals that there are very few images of malignant BC and a considerable number of images of normal Breast. Therefore, an effective segmentation approach should be applied to the breast cancer data before classifying the breast ultrasound images into two classes, namely malignant and normal. The segmented images may then be used by the proposed algorithm to accurately detect and identify normal and cancerous images. Furthermore, future research might be designed to answer clinically relevant inquiries. Moreover, In future we will explore models like ViT for BC detection. The success of improved DL algorithms could benefit radiologists and oncologists in accurately detecting BC from MRI and CT scans. However, the findings reported in this paper can assist professionals in making the right decision for their models, obviating the necessity for a thorough search. Using deep neural networks solves various problems connected with model training. It allows us to create effective models for BC diagnosis, which helps in early detection and care.  
[PERSON] and [PERSON] developed the method; [PERSON], [PERSON], [PERSON] and [PERSON] performed the experiments and analysis, and [PERSON], [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
P. Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R54), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
Not applicable.