 This study focused on using transfer learning to improve the accuracy of breast cancer detection and histopathology imaging. It compared two different 3D unit models, one that had been pre-trained on an extensive medical image segmentation dataset and another that had been fine-tuned on the same dataset. The results showed that the fine-tuned model outperformed the simple model by achieving a lower loss value and higher accuracy on the testing data. Additionally, the authors explored several data augmentation methods to further improve the model's performance. Overall, the proposed approach demonstrated great promise for improving the accuracy of breast cancer detection in histopathology imaging. This article was authored by Samon Khalil, Yurusin Awaz, Zubariah, and others.