SwiF-YOLO: A Deep Learning Method for Lung Nodule Detection
DOI:
https://doi.org/10.54097/rcx9h636Keywords:
Lung Nodule Detection, Object Detection, Medical Image Processing, Deep LearningAbstract
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.
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Sung H, Ferlay J, Siegel R L, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries[J]. CA: A Cancer Journal for Clinicians, 2021, 71(3): 209-249.
Liu K. Stbi-yolo: A real-time object detection method for lung nodule recognition[J]. IEEE Access, 2022, 10: 75385-75394.
Lu S, Yu Y, Yang Y. Retrospect and Prospect for Lung Cancer in China: Clinical Advances of Immune Checkpoint Inhibitors[J]. Oncologist, 2019, 24: S21-S30.
Henschke C I, Mccauley D I, Yankelevitz D F, et al. Early Lung Cancer Action Project: overall design and findings from baseline screening[J]. Lancet (London, England), 1999, 354(9173): 99-105.
Zhang G, Jiang S, Yang Z, et al. Automatic nodule detection for lung cancer in CT images: A review[J]. Computers in Biology and Medicine, 2018, 103: 287-300.
Haider W, Sharif M, Raza M. Achieving accuracy in early stage tumor identification systems based on image segmentation and 3D structure analysis[J]. Computer Engineering and Intelligent Systems, 2011, 2(6): 96-102.
Abubaker A, Qahwaji R S, Aqel M, et al. Average row thresholding method for mammogram segmentation[J]. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2005, 2005: 3288-91.
Girshick R. Fast r-cnn[C]. Proceedings of the IEEE international conference on computer vision, 2015: 1440-1448.
Ren S, He K, Girshick R, et al. Faster r-cnn: Towards real-time object detection with region proposal networks[J]. Advances in neural information processing systems, 2015, 28.
He K, Gkioxari G, Dollár P, et al. Mask r-cnn[C]. Proceedings of the IEEE international conference on computer vision, 2017: 2961-2969.
Duan K, Bai S, Xie L, et al. Centernet: Keypoint triplets for object detection[C]. Proceedings of the IEEE/CVF international conference on computer vision, 2019: 6569-6578.
Law H, Deng J. Cornernet: Detecting objects as paired keypoints [C]. Proceedings of the European conference on computer vision (ECCV), 2018: 734-750.
Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[J]. Advances in neural information processing systems, 2017, 30.
Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16x16 words: Transformers for image recognition at scale[J]. arXiv preprint arXiv:2010.11929, 2020.
Liu Z, Lin Y, Cao Y, et al. Swin transformer: Hierarchical vision transformer using shifted windows[C]. Proceedings of the IEEE/CVF international conference on computer vision, 2021: 10012-10022.
Liu S, Huang D, Wang Y. Learning spatial fusion for single-shot object detection[J]. arXiv preprint arXiv:1911.09516, 2019.
Setio A a A, Traverso A, De Bel T, et al. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge[J]. Medical image analysis, 2017, 42: 1-13.
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