Deep Learning for Gastric Pathology Detection in Endoscopic Images

Computer-aided diagnosis of cancer based on endoscopic image analysis is a promising area in the field of computer vision and machine learning. Convolutional neural networks are one of the most popular approaches in endoscopic image analysis. This paper presents the algorithm of pathology detection in endoscopic images of gastric lesions based on convolutional neural network. Training and testing of the algorithm was carried out on the NVIDIA DGX-1 supercomputer using endoscopic images from the test base, assembled together with the Yaroslavl Regional Cancer Hospital. As a result of experiments, the mAP metric was calculated and the value was 0.875, which is a high result for the task of object detection in images.