Improved YOLOv8 Model for Multi-type Lung Nodule Detection - BAO Qiangqiang, TANG Siyuan, LI Qingqian, WANG Naiyu, YANG Min, GU Yu, ZHAO Jinliang, GAO Jingbo, WANG Jiaxin, QU Yuhan (Jisuanji kexue yu tansuo)
Currently, lung nodule testing is usually a single type of testing for solid lung nodules, and different kinds of lung nodules correspond to multiple types of lung cancer. Multi-type testing can help improve the overall detection rate of lung cancer and enhance the cure rate. Targeted improvements to the YOLOv8 model are made to enable the detection of multiple types of lung nodules, including solid, mixed, and ground glass. Firstly, the RepViTCAA module is proposed to improve the C2f module of the main part to enhance the accuracy of tiny lung nodule detection and the lightweight design of the model. Secondly, the ECLA-HSFPN module is proposed to reconstruct the feature fusion part of the model to improve the scale-invariant lung nodule detection accuracy. Then, the KAN network is integrated into the model further to improve the detection accuracy of tiny lung nodules and enhance the generalization ability of the model based on the strong learning ability of nonlinear features of the KAN network. Finally, based on the Inner-IoU auxiliary frame idea, the CIoU loss function is improved to solve the problem of unfixed lung nodule scale and enhance the model detection accuracy. Tested on the LUNA16 dataset, the improved model has improved all evaluation indices compared with the original model and mainstream models such as YOLOv9 and RT-DETR. The improved model has better detection performance than the original model when tested on a specialized dataset of four types (solid, ground glass, mixed, and microminiature) of lung nodules. The generalizability is tested on a mixed dataset of LUNA16 and local hospitals, and the improved model has strong generalization ability. The improvement of the model is more effective for the task of detecting multiple types of lung nodules and it can accurately detect different types of lung nodules.