ELCT-YOLO: An efficient one-stage model for automatic lung tumor detection based on CT images
Research on lung cancer automatic detection using deep learning algorithms has achieved good results but, due to the complexity of tumor edge features and possible changes in tumor positions, it is still a great challenge to diagnose patients with lung tumors based on computed tomography (CT) images. In order to solve the problem of scales and meet the requirements of real-time detection, an efficient one-stage model for automatic lung tumor detection in CT Images, called ELCT-YOLO, is presented in this paper. Instead of deepening the backbone or relying on a complex feature fusion network, ELCT-YOLO uses a specially designed neck structure, which is suitable to enhance the multi-scale representation ability of the entire feature layer. At the same time, in order to solve the problem of lacking a receptive field after decoupling, the proposed model uses a novel Cascaded Refinement Scheme (CRS), composed of two different types of receptive field enhancement modules (RFEMs), which enables expanding the effective receptive field and aggregate multi-scale context information, thus improving the tumor detection performance of the model. The experimental results show that the proposed ELCT-YOLO model has strong ability in expressing multi-scale information and good robustness in detecting lung tumors of various sizes.
History
Publication
Mathematics 2023, 11, 2344Publisher
MDPIOther Funding information
This publication has emanated from joint research conducted with the financial support of the S&T Major Project of the Science and Technology Ministry of China under the Grant No. 2017YFE0135700 and the Bulgarian National Science Fund (BNSF) under the Grant No. KΠ-06-ИΠ?KИTAЙ/1 (KP-06-IP-CHINA/1Sustainable development goals
- (3) Good Health and Well-being
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Department or School
- Electronic & Computer Engineering