超快荧光共焦显微镜是乳腺癌检测的一种假设方法，因为它有可能实现细胞水平组织特征的瞬时高分辨率图像。乳房X光检查和活检等传统方法费力、侵入性且效率低下；与这些方法相比，共焦显微镜具有许多优点。然而，共焦显微镜能够准确区分恶性细胞、快速检查大范围组织切片以及将组织样本光学切片成微小切片。主要目标应该是完全预防癌症，尽管及早发现癌症有助于实现这一目标。这项研究提出了一种新颖的乳腺组织病理学卷积神经网络（BHCNN），用于特征提取和递归特征消除方法，以选择最重要的特征。所提出的方法利用完整的幻灯片图像来识别受浸润性导管癌影响的区域中的组织。此外，采用迁移学习方法来增强模型在检测乳腺癌方面的性能和准确性，同时还通过修改所提出模型的最后一层来减少计算时间。结果表明，BHCNN模型在准确率方面优于其他模型，实现了98.42%的测试准确率和99.94%的训练准确率。混淆矩阵结果显示，IDC 正 ( ) 类的准确率达到 97.44%，不准确的结果为 2.56%，而 IDC 负 (-) 类的准确率达到 98.73%，不准确的结果为 1.27%。此外，该模型的验证损失小于 0.05。研究亮点：目标是使用超快荧光共聚焦显微镜开发一个创新框架，特别是针对乳腺癌诊断这一具有挑战性的问题。该框架将从显微镜中提取基本特征，并采用梯度循环单元进行检测。拟议的研究通过提供可靠且有弹性的乳腺癌精确诊断系统，在增强医学成像方面具有巨大潜力，从而推动最先进医疗技术的进步。在通过所提出的模型检索特征后，使用 BHRFE 优化技术确定最合适的特征。最后，所选择的特征被集成到所提出的方法中，然后使用 GRU 深度模型进行分类。上述研究通过提供复杂而可靠的系统来精确评估乳腺癌，在改善医学成像方面具有巨大潜力，从而推动尖端医疗技术的发展。© 2024 Wiley periodicals LLC。

Ultrafast fluorescent confocal microscopy is a hypothetical approach for breast cancer detection because of its potential to achieve instantaneous, high-resolution images of cellular-level tissue features. Traditional approaches such as mammography and biopsy are laborious, invasive, and inefficient; confocal microscopy offers many benefits over these approaches. However, confocal microscopy enables the exact differentiation of malignant cells, the expeditious examination of extensive tissue sections, and the optical sectioning of tissue samples into tiny slices. The primary goal should be to prevent cancer altogether, although detecting it early can help achieve that objective. This research presents a novel Breast Histopathology Convolutional Neural Network (BHCNN) for feature extraction and recursive feature elimination method for selecting the most significant features. The proposed approach utilizes full slide images to identify tissue in regions affected by invasive ductal carcinoma. In addition, a transfer learning approach is employed to enhance the performance and accuracy of the models in detecting breast cancer, while also reducing computation time by modifying the final layer of the proposed model. The results showed that the BHCNN model outperformed other models in terms of accuracy, achieving a testing accuracy of 98.42% and a training accuracy of 99.94%. The confusion matrix results show that the IDC positive (+) class achieved 97.44% accuracy and 2.56% inaccurate results, while the IDC negative (-) class achieved 98.73% accuracy and 1.27% inaccurate results. Furthermore, the model achieved less than 0.05 validation loss. RESEARCH HIGHLIGHTS: The objective is to develop an innovative framework using ultra-fast fluorescence confocal microscopy, particularly for the challenging problem of breast cancer diagnosis. This framework will extract essential features from microscopy and employ a gradient recurrent unit for detection. The proposed research offers significant potential in enhancing medical imaging through the provision of a reliable and resilient system for precise diagnosis of breast cancer, thereby propelling the progression of state-of-the-art medical technology. The most suitable feature was determined using BHRFE optimization techniques after retrieving the features by proposed model. Finally, the features chosen are integrated into a proposed methodology, which is then classified using a GRU deep model. The aforementioned research has significant potential to improve medical imaging by providing a complex and reliable system for precise evaluation of breast cancer, hence advancing the development of cutting-edge medical technology.© 2024 Wiley Periodicals LLC.