基于卷积神经网络的直肠癌淋巴结转移的智能判断模型研究

近年来，直肠癌的患病率普遍提高，准确判断直肠癌周围淋巴结的转移情况，能显著提高患者的治愈概率。通过建立数学模型，运用卷积神经网络模型进行智能判断直肠癌淋巴结转移情况。以直肠肿瘤患者动脉期的电子计算机断层扫描(computed tomography, CT)图像和肿瘤掩模图为样本，建立基于U-Net的CT图像识别分割模型，通过下、上采样分割出直肠肿瘤所在的区域，对肿瘤区域已转移淋巴结的普遍特征进行深度挖掘，利用尺度不变特征变换匹配(scale invarian feature transform, SIFT)算法自动找出肿瘤区域特征点，以及传统的特征纹理、面积、周长和体素强度，分别送入传统卷积神经网络模型和改进后的卷积神经网络模型VGG16网络模型，进行训练、预测、对比。结果表明：传统卷积神经网络模型的准确率在75.32%,而改进后的VGG16网络模型准确率在90.04%,可见，VGG16网络模型对直肠癌淋巴结转移情况的预测效果更好。

Study on intelligent judgment model of rectal cancer lymph node metastasis based on convolution neural network

In recent years, the prevalence of rectal cancer has generally increased, and accurate judgment of lymph node metastasis around rectal cancer can often significantly improve the patient''s cure probability. In this paper, the convolutional neural network model was used to determine the lymph node metastasis of rectal cancer intelligently by establishing a mathematical model. A CT image recognition and segmentation model based on U-Net was established based on CT images and tumor mask samples of patients with rectal cancer in the arterial phase, and the region of rectal tumor was separated by down-sampling and up-sampling. Then the tumor area has the common characters of the metastasis lymph nodes depth excavation, feature points by using SIFT algorithm automatically identify tumor area, as well as the characteristics of the traditional texture, area, circle skillfully voxel intensity, respectively, into the traditional convolution neural network model and the improved convolution VGG16 network model, neural network model training, prediction, and comparison. Finally, it is concluded that the accuracy of the traditional convolutional neural network model is 75.32%, while the accuracy of the improved VGG16 network model is 90.04%. Therefore, the VGG16 network model has a better prediction effect on lymph node metastasis of rectal cancer. Its advantages of simplicity, efficiency and universality enable the VGG16 network model to better predict lymph node metastasis.