Breast cancer is a prevalent form of cancer among women, with over 1.5 million women being diagnosed each year. Unfortunately, the survival rates for breast cancer patients in certain third-world countries, like South Africa, are alarmingly low, with only 40% of diagnosed patients surviving beyond five years. The inadequate availability of resources, including qualified pathologists, delayed diagnoses, and ineffective therapy planning, contribute to this low survival rate. To address this pressing issue, medical specialists and researchers have turned to domain-specific AI approaches, specifically deep learning models, to develop end-to-end solutions that can be integrated into computer-aided diagnosis (CAD) systems. By improving the workflow of pathologists, these AI models have the potential to enhance the detection and diagnosis of breast cancer. This research focuses on evaluating the performance of various cutting-edge convolutional neural network (CNN) architectures in comparison to a relatively new model called the Vision Trans-former (ViT). The objective is to determine the superiority of these models in terms of their accuracy and effectiveness. The experimental results reveal that the ViT models outperform the other selected state-of-the-art CNN architectures, achieving an impressive accuracy rate of 95.15%. This study signifies a significant advancement in the field, as it explores the utilization of data augmentation and other relevant preprocessing techniques in conjunction with deep learning models for the detection and diagnosis of breast cancer using datasets of Breast Cancer Histopathological Image Classification.

翻译：乳腺癌是女性中常见的癌症类型，每年有超过150万女性被确诊。然而，在南非等某些第三世界国家，乳腺癌患者的存活率低得令人担忧，仅有40%的确诊患者能够存活超过五年。包括合格病理学家在内的资源不足、诊断延迟以及治疗规划无效等因素导致了这一低存活率。为应对这一紧迫问题，医学专家和研究人员转向特定领域的人工智能方法，特别是深度学习模型，以开发可集成到计算机辅助诊断（CAD）系统中的端到端解决方案。通过改善病理学家的诊断流程，这些AI模型有望提升乳腺癌的检测与诊断水平。本研究聚焦于评估多种前沿卷积神经网络（CNN）架构与一种相对较新的模型——视觉Transformer（ViT）的性能对比。目标是确定这些模型在准确性和有效性方面的优劣。实验结果显示，ViT模型优于其他选定的最先进CNN架构，达到了95.15%的惊人准确率。这项研究标志着该领域的重大进展，因为它探索了数据增强及其他相关预处理技术与深度学习模型的结合使用，利用乳腺癌组织病理学图像分类数据集进行检测与诊断。