ELCT-YOLO: An Efficient One-Stage Model for Automatic Lung Tumor Detection Based on CT Images

Zhanlin Ji, Jianyong Zhao, Jinyun Liu, Xinyi Zeng, Haiyang Zhang, Xueji Zhang () and Ivan Ganchev ()
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Zhanlin Ji: Hebei Key Laboratory of Industrial Intelligent Perception, College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
Jianyong Zhao: Hebei Key Laboratory of Industrial Intelligent Perception, College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
Jinyun Liu: Hebei Key Laboratory of Industrial Intelligent Perception, College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
Xinyi Zeng: Hebei Key Laboratory of Industrial Intelligent Perception, College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
Haiyang Zhang: Department of Computing, Xi’an Jiaotong-Liverpool University, Suzhou 215000, China
Xueji Zhang: School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen 518060, China
Ivan Ganchev: Telecommunications Research Centre (TRC), University of Limerick, V94 T9PX Limerick, Ireland

Mathematics, 2023, vol. 11, issue 10, 1-21

Abstract: 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.

Keywords: lung cancer; tumor; CT image; one-stage detector; YOLO; multi-scale; receptive field (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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