  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.
