References
[1] Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229-63.
[2] Al-Shabi M, Lee HK, Tan M. Gated-dilated networks for lung nodule classification in CT scans. IEEE Access. 2019;7:178827-38.
[3] Guo N, Bai Z. Multi-scale pulmonary nodule detection by fusion of cascade R-CNN and FPN. In: 2021 International Conference on Computer Communication and Artificial Intelligence (CCAI). IEEE; 2021:16-9.
[4] Harsono IW, Liawatimena S, Cenggoro TW. Lung nodule detection and classification from Thorax CT-scan using RetinaNet with transfer learning. J King Saud Univ Comput Inf Sci. 2022;34(3):567-77.
[5] Cai L, Long T, Dai Y, et al. Mask R-CNN-based detection and segmentation for pulmonary nodule 3D visualization diagnosis. IEEE Access. 2020;8:44400-9.
[6] Li X, Jin W, Li G, et al. Asymmetric Convolutional Kernel YOLO V2 Network for Pulmonary Nodule Detection in CT Images. Chin J Biomed Eng. 2019;38(4):401-8.
[7] Xu K, Jiang H, Tang W. A New Object Detection Algorithm Based on YOLOv3 for Lung Nodules. In: Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence. 2020:233-9.
[8] Manokaran J, Mittal R, Ukwatta E. Pulmonary nodule detection in low dose computed tomography using a medical-to-medical transfer learning approach. J Med Imaging. 2024;11(4):044502.
[9] Mammeri S, Amroune M, Haouam MY, et al. Early detection and diagnosis of lung cancer using YOLO v7, and transfer learning. Multimed Tools Appl. 2024;83(10):30966-80.
[10] Xiong Y, Deng L, Wang Y. Pulmonary nodule detection based on model fusion and adaptive false positive reduction. Expert Syst Appl. 2024;238:121890.
[11] Liu S, Qi L, Qin H, et al. Path aggregation network for instance segmentation. In: Proceedings of the IEEE Conference on Com-puter Vision and Pattern Recognition. 2018:8759-68.
[12] Sunkara R, Luo T. No more strided convolutions or pooling: A new CNN building block for low-resolution images and small objects. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Cham: Springer; 2022:443-59.
[13] Ding X, Zhang Y, Ge Y, et al. UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio Video Point Cloud Time-Series and Image Recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024:5513-24.
[14] Zhao G, Ge W, Yu Y. GraphFPN: Graph feature pyramid network for object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021:2763-72.
[15] Zhang K, Li Z, Zhang F, et al. Pan-sharpening based on transformer with redundancy reduction. IEEE Geosci Remote Sens Lett. 2022;19:1-5.
[16] Hidayatullah P, Syakrani N, Sholahuddin MR, et al. YOLOv8 to YOLO11: A Comprehensive Architecture In-depth Comparative Review. arXiv. 2025;2501.13400.
[17] Setio AAA, 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. Med Image Anal. 2017;42:1-13.
[18] Liu Z, Lv Q, Li Y, et al. Medaugment: universal automatic data augmentation plug-in for medical image analysis. arXiv. 2023;2306.17466.
[19] Girshick R. Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision. 2015:1440-8.
[20] Liu W, Anguelov D, Erhan D, et al. Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14. Springer; 2016:21-37.
[21] Bochkovskiy A, Wang CY, Liao HYM. Yolov4: Optimal speed and accuracy of object detection. arXiv. 2020;2004.10934.
[22] Wang CY, Bochkovskiy A, Liao HYM. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023:7464-75.
[23] Wang CY, Yeh IH, Liao HYM. Yolov9: Learning what you want to learn using programmable gradient information. In: European Conference on Computer Vision. Cham: Springer; 2025:1-21.
[24] Wang A, Chen H, Liu L, et al. Yolov10: Real-time end-to-end object detection. arXiv. 2024;2405.14458.
[25] Chen M, Li Y, Yun J, et al. Pulmonary nodule detection algorithm based on KCCS-YOLOv4. J Changchun Univ. 2023;44(5):424-33. doi:10.15923/j.cnki.cn22-1382/t.2023.5.07.
[26] Liu K. Stbi-yolo: A real-time object detection method for lung nodule recognition. IEEE Access. 2022;10:75386-94.
[27] Ji Z, Wu Y, Zeng X, et al. Lung nodule detection in medical images based on improved YOLOv5s. IEEE Access. 2023;11:76371-87.
[28] Liu Y, Ao Y. Deformable attention mechanism-based YOLOv7 structure for lung nodule detection. J Supercomput. 2024;80(17):25450-69.
[29] Wu X, Zhang H, Sun J, et al. YOLO-MSRF for lung nodule detection. Biomed Signal Process Control. 2024;94:106318.
[30] Şaman C, Çelikbaş Ş. YOLOv8-based lung nodule detection: a novel hybrid deep learning model proposal. Int Res J Eng Technol. 2023;10(8):230-7.
[31] Wang M, Yang Z, Zhao R, et al. CPLOYO: A Pulmonary Nodule Detection Model with Multi-Scale Feature Fusion and Nonlinear Feature Learning. arXiv. 2025;2503.10045.