Object detection of pulmonary nodules based on fast R-CNN and CBAM

With the continuous progress of society, people's lifestyle and quality of life are constantly changing, although the overall life expectancy of human beings has been extended, but lung diseases are still a serious threat to human health. In order to improve the object detection performance of lung nodules, the Fast R-CNN model was improved. By adding the CBAM attention mechanism module to the convolutional layer of the ResNet-50 and VGG16 feature extraction models, the model focuses more on the key feature extraction in the image. Due to the small size of pulmonary nodules in CT images, a object size selection box based on statistical data was introduced, and the improved Fast R-CNN-VGG16- CBAM model was used in the experiment with VGG16 as the backbone. Compared with the original model, the precision is increased by 6.01 percentage points, the recall rate is increased by 3.34 percentage points, and the mAP value is increased by 4.54 percentage points, and the improved Fast R-CNN-Resnet50-CBAM model is improved by 2.71 percentage points, the recall rate is increased by 4.6 percentage points, and the mAP value is increased by 4.03 percentage points. In addition, compared with the first-stage detection model YOLOV3, the improved Fast R-CNN-Resnet50- CBAM model has 5.2 percentage points higher precision and 13.36 percentage points higher mAP value. Experimental results show that the designed Fast R-CNN-Resnet50-CBAM model can effectively improve the performance of lung nodule object detection.