## 5 Conclusions  
This study demonstrates the promising results of transfer learning in enhancing the categorization of breast histopathology images for invasive ductal carcinoma (a prevalent subtype of breast cancer) detection. The study improved upon the accuracy of a basic 3D U-Net model (87%) in identifying ductal cancer by fine-tuning a pre-trained 3D U-Net model with 97% accuracy. The findings show that transfer learning successfully utilizes the features gained from a previously trained model to boost performance on a different task. The outcomes reveal that the classification model can be fine-tuned to a higher extent. Although the current model has shown good accuracy, there is still room for improvement as the difference between the training and testing accuracy suggests that the classification model might have become overly specific to the training data and may not generalize well. More research in this direction could help push the boundaries of computer-assisted diagnosis by enhancing the efficiency with training over larger sets of cancer imaging data. The use of transfer learning and 3D U-Net models in breast cancer imaging can have significant implications for society. The early and accurate diagnosis of breast cancer can greatly improve a patient's chances of successful treatment and recovery. By improving the accuracy and efficiency of ductal carcinoma classification, healthcare professionals can make more informed decisions about treatment options and improve patient outcomes. Additionally, the use of transfer learning and 3D U-Net models can potentially reduce the workload and increase the productivity of radiologists. This allows for a more efficient diagnosis and effective treatment of breast cancer patients. Ultimately, this technology has the potential to make a meaningful impact on society by improving the identification and treatment of breast cancer and potentially other cancer types as well.  
Conceptualization, methodology, data curation, formal analysis, S.K., U.N., Z. and Z.M.; software, visualization, investigation, validation, M.Z.u.R., M.F.Q., S.A. and A.M.; resources, supervision, funding acquisition, project administration, A.A. and K.A. All authors were involved in writing, reviewing, and editing the manuscript. All authors have read and agreed to the published version of the manuscript. This work is supported by the Researchers Supporting Project (RSPD2023R642), King Saud University, Riyadh, Saudi Arabia.  
Publicly available datasets were analyzed in this study. The data can be found here: [[https://www.kaggle.com/datasets/paultimothymooney/breast-histopathology-images](https://www.kaggle.com/datasets/paultimothymooney/breast-histopathology-images)]([https://www.kaggle.com/datasets/paultimothymooney/breast-histopathology-images](https://www.kaggle.com/datasets/paultimothymooney/breast-histopathology-images)) (accessed on 3 January 2023). The authors are thankful to the Researchers Supporting Project (RSPD2023R642), King Saud University, Riyadh, Saudi Arabia.  
The author declares no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Dataset** & **Model** & **Accuracy** \\ \hline [PERSON] et al. [41] & Histopathological data & AOADL-HBCC & 95\% \\  & of breasts & & \\ [PERSON] et al. [23] & Histopathological data & CNN & 91.5\% \\  & of breasts & & \\ [PERSON] et al. [12] & Histopathological data & AlexNet & 90\% \\  & of breasts & & \\ [PERSON] et al. [8] & Histopathological data & & \\  & of breasts & & \\ This study & Histopathological data & & \\  & of breasts & & \\ \hline \end{tabular}
\end{table}
Table 2: Detailed comparison of prior advanced studies and the proposed work for the classification of invasive ductal carcinoma.