SPE-YOLO: A deep learning model focusing on small pulmonary embolism detection

Abstract

Objectives

By developing the deep learning model SPE-YOLO, the detection of small pulmonary embolism has been improved, leading to more accurate identification of this condition. This advancement aims to better serve medical diagnosis and treatment.

Methods

This retrospective study analyzed images of 142 patients from Tianjin Medical University General Hospital using YOLOv8 as the foundational framework. Firstly, a small detection head P2 was added to better capture and identify small targets. Secondly, the SEAttention mechanism was integrated into the model to enhance focus on crucial features and optimize detection accuracy. Lastly, the feature extraction process was refined by introducing ODConv convolution to capture more comprehensive contextual information, thereby enhancing the detection performance of small pulmonary embolisms. The model's practical application ability was evaluated using 2000 cases from the RSNA dataset as an external test set.

Results

In the Tianjin test set, our model achieved a precision of 84.20 % and an accuracy of 81.50 %. This represents an improvement of approximately 5 % and 4 % respectively compared to the original YOLOv8. F1 scores, recall rates and average accuracy have also increased by 4 %, 5 %, 6 %, respectively. In data from the external validation set of RSNA, SPE-YOLO exhibited its effectiveness with a sensitivity of 90.70 % and an accuracy of 86.45 %.

Conclusion

The SPE-YOLO algorithm demonstrates strong capability in identifying small pulmonary embolisms, offering clinicians a more accurate and efficient diagnostic tool. This advancement is expected to enhance the quality of pulmonary embolism diagnosis and support the progress of medical services.