医学图像的分割在正确识别和管理不同疾病中起着关键作用。在这项研究中,我们提出了一种新的分割方法,可以解决计算机断层扫描(CT)图像中复杂的器官形状所带来的困难,特别是针对肺,乳房,和胃癌。我们建议的方法,Resio-InceptionU-Net和深度集群识别(RIUDCR),使用剩余盗梦空间架构,它结合了剩余连接和起始块的力量,实现了尖端的分段性能,同时降低了过拟合的风险。我们提出了描述设计的数学方程和函数,包括UC-Net系统内的编码和解码步骤。此外,我们提供了强有力的测试结果,证明了我们方法的有效性。通过对各种数据集的彻底测试,我们的方法经常胜过当前的技术,在器官任务分割中实现了惊人的精度和稳定性。这些结果表明了我们的残差起始体系结构在更好的医学图像分析中的前景。总之,我们的研究不仅显示了最先进的细分方法,而且还通过彻底的测试增强了其实用性。在医学图像分割中包含残余起始体系结构为改善疾病计划的识别和管理提供了很好的可能性。
Segmentation of medical images plays a key role in the correct identification and management of different diseases. In this study, we present a new segmentation method that meets the difficulties posed by sophisticated organ shapes in computed tomography (CT) images, particularly targeting lung, breast, and gastric cancers.
Our suggested methods, Resio-
Inception U-Net and Deep Cluster Recognition (RIUDCR), use a Residual
Inception Architecture, which combines the power of residual connections and
inception blocks to achieve cutting-edge segmentation performance while reducing the risk of overfitting.
We present mathematical equations and functions that describe the design, including the encoding and decoding steps within the UC-Net system. Furthermore, we provide strong testing results that show the effectiveness of our method. Through thorough testing on varied datasets, our method regularly beats current techniques, achieving amazing precision and stability in organ task segmentation. These results show the promise of our residual
inception architecture in better medical picture analysis.
In summary, our research not only shows a state-of-the-art segment methodology but also reinforces its usefulness through thorough testing. The inclusion of residual
inception architecture in medical picture segmentation offers good possibilities for improving the identification and management of disease planning.