Classification of Esophageal Lesions in Endoscopic Images Using Convolutional Neural Network

Gastrointestinal endoscopy is the major technique used for the screening of esophageal cancer. Due to individual variations and visual similarities of lesions in shapes, colors and textures under the endoscopy, the efficiency and accuracy of diagnosing esophageal squamous cell carcinoma is significantly dependent on the experience and proficiency of gastroenterologists. Lesions are often misdiagnosed especially under the white light endoscopy. To address these problems, a CNN architecture that integrates the bilinear pooling operation and attention mechanism is proposed to classify esophageal lesions in white light endoscopic images. The ResNet50 network is chosen as the backbone network structure. The newly designed global channel attention module is adopted to recalibrate features between different channels. The bilinear pooling operation is applied to merge features of different layers for improving the representation quality. Experiments are conducted on white light endoscopic images of 2, 101 clinical cases collected from multiple hospitals. In this experimental results, the proposed model achieves an accuracy of 94.2%, a sensitivity of 95.4% and a specificity of 98.8% at the image level, while at the patient level, the accuracy is 96.9%, the sensitivity is 98.7% and the specificity is 95.9%. The comprehensive evaluation shows that the proposed model specifically has advantages in classifying esophageal lesions in endoscopic images and outperforms other state-of-the-art methods. It can effectively improve the accuracy of diagnosing esophageal squamous cell carcinoma, with a high robustness. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.