Convolutional Neural Network for Early Detection of Gastric Cancer by Endoscopic Video Analysis

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 the endoscopic image analysis. The paper presents an endoscopic video analysis algorithm based on the use of convolutional neural network. To analyze the quality of the algorithm on the video data from the endoscope, the intersection over union (IoU) metric for object detection is used. The experimental results shows that the average value of IoU coefficient for the developed algorithm is 0.767, which corresponds to a high degree of intersection of areas identified by an expert and the algorithm.