Esophageal cancer detection based on classification of gastrointestinal CT images using improved Faster RCNN

Purpose: Esophageal cancer is a common malignant tumor in life, which seriously affects human health. In order to reduce the work intensity of doctors and improve detection accuracy, we proposed esophageal cancer detection using deep learning. The characteristics of deep learning: association and structure, ac-tivity and experience, essence and variation, migration and application, value and evaluation. Method: The improved Faster RCNN esophageal cancer detection in this paper introduces the online hard example mining (OHEM) mechanism into the system, and the experiment used 1520 gastrointestinal CT images from 421 patients. Then, we compare the overall performance of Inception-v2, Faster RCNN, and improved Faster RCNN through F-1 measure, mean average precision (mAP), and detection time. Results: The experiment shows that the overall performance of the improved Faster RCNN is higher than the other two networks. The F-1 measure of our method reaches 95.71%, the mAP reaches 92.15%, and the detection time per CT is only 5.3s. Conclusion: Through comparative analysis on the esophageal cancer image data set, the experimental re-sults show that the introduction of online hard example mining mechanism in the Faster RCNN algorithm can improve the detection accuracy to a certain extent. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study improved esophageal cancer detection using Faster RCNN by introducing an online hard example mining mechanism, achieving higher detection accuracy compared to other networks. The experimental results demonstrate the effectiveness of the improved algorithm in enhancing overall performance.