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.