利用超快荧光共聚焦显微镜进行乳腺组织病理学成像,以识别早期癌症病灶。

IF 2 3区 工程技术 Q2 ANATOMY & MORPHOLOGY
Muhammad Mujahid, Amjad Rehman Khan, Mahyar Kolivand, Faten S Alamri, Tanzila Saba, Saeed Ali Omer Bahaj
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引用次数: 0

摘要

超快荧光共聚焦显微镜是一种检测乳腺癌的假想方法,因为它有可能获得细胞级组织特征的即时、高分辨率图像。乳房 X 射线照相术和活组织检查等传统方法费力、侵入性强、效率低;而共聚焦显微镜与这些方法相比具有许多优势。然而,共聚焦显微镜可以准确区分恶性细胞,快速检查大面积组织切片,并将组织样本光学切片成小片。虽然早期检测有助于实现这一目标,但首要目标应该是彻底预防癌症。本研究提出了一种用于特征提取的新型乳腺组织病理学卷积神经网络(BHCNN),以及用于选择最重要特征的递归特征消除方法。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区域的组织。此外,还采用了迁移学习方法来提高模型检测乳腺癌的性能和准确性,同时还通过修改拟议模型的最后一层来减少计算时间。结果表明,BHCNN 模型的准确性优于其他模型,测试准确率达到 98.42%,训练准确率达到 99.94%。混淆矩阵结果显示,IDC 阳性(+)类的准确率为 97.44%,不准确率为 2.56%,而 IDC 阴性(-)类的准确率为 98.73%,不准确率为 1.27%。此外,该模型的验证损失小于 0.05。研究亮点:目标是利用超快荧光共聚焦显微镜开发一个创新框架,尤其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,并采用梯度递归单元进行检测。拟议的研究通过提供一个可靠、灵活的系统来精确诊断乳腺癌,从而推动最先进医疗技术的发展,为增强医学成像提供了巨大的潜力。在利用拟议模型检索特征后,使用 BHRFE 优化技术确定了最合适的特征。最后,将所选特征整合到提议的方法中,然后使用 GRU 深度模型对其进行分类。上述研究为精确评估乳腺癌提供了一个复杂而可靠的系统,从而推动了尖端医疗技术的发展,在改善医学成像方面具有巨大潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Breast histopathological imaging using ultra-fast fluorescence confocal microscopy to identify cancer lesions at early stage.

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.

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来源期刊
Microscopy Research and Technique
Microscopy Research and Technique 医学-解剖学与形态学
CiteScore
5.30
自引率
20.00%
发文量
233
审稿时长
4.7 months
期刊介绍: Microscopy Research and Technique (MRT) publishes articles on all aspects of advanced microscopy original architecture and methodologies with applications in the biological, clinical, chemical, and materials sciences. Original basic and applied research as well as technical papers dealing with the various subsets of microscopy are encouraged. MRT is the right form for those developing new microscopy methods or using the microscope to answer key questions in basic and applied research.
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