论文标题
提出了使用CNN和SVM根据舌头的颜色和舌头特征提高胃癌检测准确性的方法
Proposing method to Increase the detection accuracy of stomach cancer based on colour and lint features of tongue using CNN and SVM
论文作者
论文摘要
如今，胃癌是影响许多人生活的疾病之一。早期发现和准确性是发现这种癌症的主要和关键挑战。在本文中，提出了一种基于深卷积神经网络和支持向量机的舌头和舌头特征来提高检测癌症诊断准确性的方法。在提出的方法中，舌头区域首先通过{deep rcnn} \ color {black}递归卷积神经网络（r-cnn）\ color {black {black {black}将舌头区域与面部图像分开。经过必要的预处理后，提供了卷积神经网络的图像，并触发训练和测试操作。结果表明，所提出的方法可以正确地识别舌头以及患者的非患者。基于实验，与其他深层体系结构相比，Densenet网络的精度最高。实验结果表明，该胃癌检测网络的准确性达到91％，这表明与最先进的方法相比，方法的优势。
Today, gastric cancer is one of the diseases which affected many people's life. Early detection and accuracy are the main and crucial challenges in finding this kind of cancer. In this paper, a method to increase the accuracy of the diagnosis of detecting cancer using lint and colour features of tongue based on deep convolutional neural networks and support vector machine is proposed. In the proposed method, the region of tongue is first separated from the face image by {deep RCNN} \color{black} Recursive Convolutional Neural Network (R-CNN) \color{black}. After the necessary preprocessing, the images to the convolutional neural network are provided and the training and test operations are triggered. The results show that the proposed method is correctly able to identify the area of the tongue as well as the patient's person from the non-patient. Based on experiments, the DenseNet network has the highest accuracy compared to other deep architectures. The experimental results show that the accuracy of this network for gastric cancer detection reaches 91% which shows the superiority of method in comparison to the state-of-the-art methods.
