Lung Nodule Detection in Medical Images Based on Improved YOLOv5s

Lung cancer has the highest morbidity and mortality rate worldwide. The early detection of pulmonary nodules in lungs can help reduce the incidence of lung cancer. However, due to the great variance in shape, size, and location of pulmonary nodules, the detection of small nodules in medical images is very challenging. This paper proposes a novel YOLOv5-CASP model, based on YOLOv5s with the following proposed improvements: 1) incorporating improved Convolutional Block Attention Modules (CBAM) to suppress the interference features of the medical images through a channel dimension and spatial dimension, and to improve the detection performance of the model; 2) substituting the Spatial Pyramid Pooling - Fast (SPPF) module of YOLOv5s with an improved Atrous Spatial Pyramid Pooling (ASPP) module as to increase the model's receptive field for images of different sizes and extract multi-scale contextual information for improving its performance on detecting small lung nodules; and 3) introducing a Contextual Transformer (CoT) module to optimize part of the CSPDarknet53 module of YOLOv5s in order to enhance the characteristics of the model while removing redundant operations extraction capacity. Experimental results conducted on two public datasets confirm that the proposed YOLOv5-CASP model outperforms the original YOLOv5s model and other five state-of-the-art models (Faster R-CNN, SSD, YOLOv4-Tiny, DETR-R50, Deformable DETR-R50), in terms of the mean average precision (mAP) and F1 score, by achieving corresponding values of 0.720 and 0.740 on the LUNA16 dataset, and 0.794 and 0.766 on the X-Nodule dataset.