Abstract

Info

Author(s):: JIANG Jiewei1; LIU Haiyang1; LIN Tongtong1; PEI Mengjie2; WEI Xumeng3; GONG Jiamin1; 4; LI Zhongwen5; 1. School of Electronic Engineering, Xian University of Posts and Telecommunications, Xian 710121, China 2. School of Computer Science and Technology, Xian University of Posts and Telecommunications, Xian 710121, China 3. School of Communications and Information Engineering, Xian University of Posts and Telecommunications, Xian 710121, China 4. School of Modern Post, Xian University of Posts and Telecommunications, Xian 710121, China 5. Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China

Keywords:: Keywords: eyelid tumor fine-grained localization dual attention mechanism residual network

Abstract:: Abstract: Eyelid tumor is a serious eye disease that leads to vision loss or even blindness. The similarity between benign and malignant characteristics makes it difficult for ophthalmologists lacking clinical experience to distinguish between them. To address the problem, a method (ResNet101_CBAM) based on two-stage target localization using fully convolutional one-stage object detection (FCOS) and residual network incorporating a dual attention mechanism is proposed to realize the automatic diagnosis of benign and malignant eyelid tumors. FCOS is used to automatically localize the overall contour of the orbit, removing the background and surrounding noise, and then finely localize the tumor lesion inside the orbit. The obtained lesion region is input into ResNet101_CBAM for the automatic diagnosis of benign and malignant eyelid tumors. The experimental results show that the average precision of the target localization algorithm for tumor lesion is 0.821, and that compared with ResNet101, ResNet101_CBAM improves the sensitivity and accuracy in eyelid tumor classification by 4.7% and 3.0%, respectively, indicating that the proposed model has superior performances in the automatic diagnosis of benign and malignant eyelid tumors.