ABSTRACT

Objective:

To apply SegNet approach to establish an auxiliary diagnosis model for lung cancer based on lung computed tomography (CT) image scores, and to explore its value in distinguishing benign and malignant lung CT images.

Methods:

We selected 240 patients, half of whom were diagnosed as early-stage lung cancer, and half were diagnosed as benign lung nodules. This paper proposes a based on SegNet recognition technology to segment images, and compares the sensitivity, specificity, accuracy, total image segmentation time, and overlap rate of Deeplab v3, VGG 19 and manual image segmentation for lung cancer.

Results:

The overlap rate of the SegNet model is 95.11%, and the overlap rate closest to manual segmentation is 95.26%. The overlap rate of Deeplab v3 and VGG 19 is much lower than that of manual segmentation. The SegNet model has a sensitivity of 98.33%, a specificity of 86.67%, an accuracy of 92.50%, and a total segmentation time of 30.42 s, which is shorter than manual segmentation.

Conclusion:

Based on SegNet recognition technology, it can effectively improve the diagnostic sensitivity of early lung cancer, and assist physicians to screen early lung cancer more effectively and quickly, which is worthy of clinical promotion.

中文翻译：

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基于SegNet的肺CT图像分割在肺癌诊断中的应用

摘要

客观的：

应用 SegNet 方法建立基于肺 CT 图像评分的肺癌辅助诊断模型，并探讨其在区分肺部 CT 图像良恶性中的价值。

方法：

我们选择了 240 名患者，其中一半被诊断为早期肺癌，一半被诊断为良性肺结节。本文提出了一种基于SegNet识别技术的图像分割方法，并比较了Deeplab v3、VGG 19和人工肺癌图像分割的敏感性、特异性、准确性、总图像分割时间和重叠率。

结果：

SegNet模型的重叠率为95.11%，最接近人工分割的重叠率为95.26%。Deeplab v3 和 VGG 19 的重叠率远低于手动分割。SegNet 模型的灵敏度为 98.33%，特异性为 86.67%，准确度为 92.50%，总分割时间为 30.42 s，比手动分割短。

结论：

基于SegNet识别技术，可有效提高早期肺癌的诊断灵敏度，辅助医生更有效、更快速地筛查早期肺癌，值得临床推广。