WTAM-YOLO: a YOLOv11-based method for pulmonary nodule detection

Lung cancer is among the malignancies with the highest incidence and mortality rates worldwide, and it poses a serious threat to human health. Increasing the accuracy of pulmonary nodule detection in CT images is essential for the early diagnosis and treatment of lung cancer. However, the grayscale characteristics of lung CT images, together with the variability in the sizes and morphologies of nodules, make the existing detection models prone to false positives and false negatives, posing challenges for achieving accurate detection. To address these problems, an improved WTAM-YOLO model based on YOLOv11 is proposed in this study. The model features four main improvements: a wavelet convolution approach to expand the receptive field, a lightweight convolutional block attention module (CBAM) to enhance the key feature representations, a hierarchical residual attention mixer (HRAMi) module to improve the multiscale detection performance of the model, and an improved exponential moving average (iEMA) module to strengthen the detail capture ability of the model and reduce the number of false positives. Experiments are conducted with a pulmonary nodule dataset acquired from the Roboflow platform and the LUNA16 dataset. Compared with those of YOLOv11, the proposed model improves the mAP@50 values by 3.4% and 2.5%, the mAP@75 values by 9.5% and 7.0%, the precision values by 4.4% and 0.7%, and the recall values by 2.3% and 4.6% based on the Roboflow and LUNA16 datasets, respectively.