Proposing method to Increase the detection accuracy of stomach cancer based on c

Proposing method to Increase the detection accuracy of stomach cancer based on colour and lint features of tongue using CNN and SVM

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.

基于CNN和SVM的基于舌头颜色和皮屑特征的提高胃癌检测准确性的方法

如今，胃癌已成为影响许多人生活的疾病之一。早期检测和准确性是发现这种癌症的主要和关键挑战。.. 提出了一种基于深度卷积神经网络和支持向量机的利用皮屑和舌头颜色特征提高癌症诊断率的方法。在提出的方法中，首先通过{深RCNN} \ color {black}递归卷积神经网络（R-CNN）\ color {black}将舌头区域与面部图像分开。经过必要的预处理后，将图像提供给卷积神经网络，并触发训练和测试操作。结果表明，所提出的方法能够正确识别非患者的舌头区域以及患者的人。根据实验，与其他深度架构相比，DenseNet网络具有最高的准确性。 （阅读更多）