Netaji Subhas University of Technology, Delhi, India.
J Imaging Inform Med. 2024 Aug;37(4):1752-1766. doi: 10.1007/s10278-024-01056-3. Epub 2024 Mar 1.
Breast cancer is recognized as a prominent cause of cancer-related mortality among women globally, emphasizing the critical need for early diagnosis resulting improvement in survival rates. Current breast cancer diagnostic procedures depend on manual assessments of pathological images by medical professionals. However, in remote or underserved regions, the scarcity of expert healthcare resources often compromised the diagnostic accuracy. Machine learning holds great promise for early detection, yet existing breast cancer screening algorithms are frequently characterized by significant computational demands, rendering them unsuitable for deployment on low-processing-power mobile devices. In this paper, a real-time automated system "Auto-BCS" is introduced that significantly enhances the efficiency of early breast cancer screening. The system is structured into three distinct phases. In the initial phase, images undergo a pre-processing stage aimed at noise reduction. Subsequently, feature extraction is carried out using a lightweight and optimized deep learning model followed by extreme gradient boosting classifier, strategically employed to optimize the overall performance and prevent overfitting in the deep learning model. The system's performance is gauged through essential metrics, including accuracy, precision, recall, F1 score, and inference time. Comparative evaluations against state-of-the-art algorithms affirm that Auto-BCS outperforms existing models, excelling in both efficiency and processing speed. Computational efficiency is prioritized by Auto-BCS, making it particularly adaptable to low-processing-power mobile devices. Comparative assessments confirm the superior performance of Auto-BCS, signifying its potential to advance breast cancer screening technology.
乳腺癌是全球女性癌症相关死亡的主要原因，这强调了早期诊断的重要性，因为早期诊断可以提高生存率。目前的乳腺癌诊断程序依赖于医疗专业人员对病理图像的手动评估。然而，在偏远或服务不足的地区，专家医疗资源的稀缺性经常影响诊断的准确性。机器学习在早期检测方面具有巨大的潜力，但现有的乳腺癌筛查算法通常具有很高的计算需求，因此不适合在低处理能力的移动设备上部署。本文提出了一种实时自动化系统“Auto-BCS”，它可以显著提高早期乳腺癌筛查的效率。该系统分为三个不同的阶段。在初始阶段，图像会经过预处理阶段，以减少噪声。然后，使用轻量级和优化的深度学习模型进行特征提取，接着是极端梯度提升分类器，该分类器被策略性地用于优化整体性能并防止深度学习模型过度拟合。该系统的性能通过关键指标进行评估，包括准确性、精度、召回率、F1 分数和推理时间。与最先进算法的对比评估证实了 Auto-BCS 优于现有模型，在效率和处理速度方面都表现出色。Auto-BCS 优先考虑计算效率，使其特别适用于低处理能力的移动设备。比较评估证实了 Auto-BCS 的优越性能，表明它有潜力推动乳腺癌筛查技术的发展。
