SwiF-YOLO: A Deep Learning Method for Lung Nodule Detection

Abstract

Lung cancer, a prevalent and lethal tumor globally, has a five-year survival rate of only 10%-16% for late-stage patients. However, early diagnosis and treatment can increase this rate to 52%. Lung nodules, as crucial indicators of early lung cancer, are challenging to detect due to their small size and similar features to other lung tissues. Therefore, developing an automatic detection method to improve the efficiency and accuracy of lung nodule detection is vital. This paper proposes a new method based on the YOLOx model, called SwiF-YOLO, to enhance the precision and efficiency of lung nodule detection. We introduced the Swin transformer to replace the main network of yolox-m, adopted the Adaptively Spatial Feature Fusion (ASFF) as the feature fusion method, and replaced the Intersection over Union (IOU) regression loss function with Generalized Intersection over Union (GIoU). These improvements aim to enhance the accuracy and efficiency of lung nodule detection, assisting doctors in diagnosing more accurately and quickly.