{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T10:01:48Z","timestamp":1784628108824,"version":"3.55.0"},"reference-count":30,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T00:00:00Z","timestamp":1784592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Pulmonary nodules are circular or irregular lesions visible on chest computed tomography (CT), and their early detection is critical for lung cancer screening. Deep learning detection algorithms have been widely adopted for pulmonary nodule diagnosis; existing lightweight models suffer from redundant network parameters and low detection accuracy for tiny lesions. To address these limitations, this study proposes an improved detection model based on YOLOv8n. First, Omni-Dimensional Dynamic Convolution (ODConv) replaces static convolution in the backbone to enhance multi-morphology nodule feature extraction. Second, the Convolutional Block Attention Module (CBAM) is embedded at multiple positions of the neck network to suppress background interference from blood vessels and normal lung parenchyma. Third, Complete Intersection over Union (CIoU) loss is substituted by Wise Intersection over Union (W-IoU) to optimize bounding box regression for hard samples with blurred boundaries. Experiments on the LUNA16 dataset show that compared with the original YOLOv8n, the proposed model improves Precision by 6.3%, Recall by 8.6%, mAP50 by 3.4%, and mAP50-95% by 2.7% while maintaining high inference speed. Additional generalization verification on the LIDC-IDRI multi-center dataset further proves the robustness of the proposed lightweight architecture, which achieves balanced accuracy and real-time performance compared with mainstream detection models.<\/jats:p>","DOI":"10.3390\/jimaging12070329","type":"journal-article","created":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T08:14:27Z","timestamp":1784621667000},"page":"329","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic Convolution Enhanced Attention Network for Pulmonary Nodule Detection"],"prefix":"10.3390","volume":"12","author":[{"given":"Shengqun","family":"Zhang","sequence":"first","affiliation":[{"name":"Faculty of Engineering, Universiti Malaysia Sarawak, Kota Samarahan 94300, Sarawak, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Annie Anak","family":"Joseph","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Universiti Malaysia Sarawak, Kota Samarahan 94300, Sarawak, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7708-6031","authenticated-orcid":false,"given":"Kho Lee","family":"Chin","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Universiti Malaysia Sarawak, Kota Samarahan 94300, Sarawak, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,21]]},"reference":[{"key":"ref_1","first-page":"229","article-title":"Global Cancer Statistics 2022: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA A Cancer J. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e64649","DOI":"10.2196\/64649","article-title":"Enhancing Diagnostic Accuracy of Lung Nodules in Chest Computed Tomography Using Artificial Intelligence: Retrospective Analysis","volume":"27","author":"Liu","year":"2025","journal-title":"J. Med. Internet Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"060003","DOI":"10.3788\/LOP57.060003","article-title":"Research Progress on Content-Based Medical Image Retrieval","volume":"57","author":"Yang","year":"2020","journal-title":"Laser Optoelectron. Prog."},{"key":"ref_4","first-page":"434","article-title":"An Automatic Detection Algorithm for Pulmonary Nodules That Integrates Multi-Scale Information","volume":"37","author":"Liu","year":"2020","journal-title":"J. Biomed. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Czerw, A., Depta\u0142a, A., Partyka, O., Pajewska, M., Wi\u015bniewska, E., Sygit, K., Wysocki, S., Cipora, E., Konieczny, M., and Bana\u015b, T. (2024). Lung Cancer Screening\u2014Trends and Current Studies. Cancers, 16.","DOI":"10.3390\/cancers16152691"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"87","DOI":"10.3390\/onco4020008","article-title":"Can Tumour Antigens Act as Biomarkers for the Early Detection of Non-Small Cell Lung Cancer?","volume":"4","author":"Mohamed","year":"2024","journal-title":"Onco"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"22447","DOI":"10.1038\/s41598-024-73435-3","article-title":"Impact of Artificial Intelligence Assistance on Pulmonary Nodule Detection and Localization in Chest CT: A Comparative Study among Radiologists of Varying Experience Levels","volume":"14","author":"Peters","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning","volume":"172","author":"Kermany","year":"2018","journal-title":"Cell"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"626.e23","DOI":"10.1016\/j.crad.2021.04.006","article-title":"Performance and Reading Time of Lung Nodule Identification on Multidetector CT with or without an Artificial Intelligence-Powered Computer-Aided Detection System","volume":"76","author":"Hsu","year":"2021","journal-title":"Clin. Radiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2043","DOI":"10.3390\/biomedinformatics4030111","article-title":"Pulmonary Nodule Detection, Segmentation and Classification Using Deep Learning: A Comprehensive Literature Review","volume":"4","author":"Marinakis","year":"2024","journal-title":"BioMedInformatics"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"705","DOI":"10.3348\/jksr.2024.0044","article-title":"Studies and Real-World Experience Regarding the Clinical Application of Artificial Intelligence Software for Lung Nodule Detection","volume":"85","author":"Kim","year":"2024","journal-title":"J. Korean Soc. Radiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6929","DOI":"10.21037\/qims-22-1297","article-title":"Application Value of a Computer-Aided Diagnosis and Management System for the Detection of Lung Nodules","volume":"13","author":"Chen","year":"2023","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"121890","DOI":"10.1016\/j.eswa.2023.121890","article-title":"Pulmonary Nodule Detection Based on Model Fusion and Adaptive False Positive Reduction","volume":"238","author":"Xiong","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1360\/TB-2022-1240","article-title":"Intelligent Medicine and Beyond","volume":"68","author":"Liu","year":"2023","journal-title":"Chin. Sci. Bull."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1007\/s00330-024-10907-0","article-title":"Deep Learning in Pulmonary Nodule Detection and Segmentation: A Systematic Review","volume":"35","author":"Gao","year":"2024","journal-title":"Eur. Radiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1080\/0952813X.2015.1020526","article-title":"Intensity-Based Statistical Features for Classification of Lungs CT Scan Nodules Using Artificial Intelligence Techniques","volume":"27","author":"Akram","year":"2015","journal-title":"J. Exp. Theor. Artif. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s11220-020-00314-2","article-title":"Airport Detection Based on Improved Faster RCNN in Large Scale Remote Sensing Images","volume":"21","author":"Yin","year":"2020","journal-title":"Sens. Imaging"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"012085","DOI":"10.1088\/1742-6596\/1827\/1\/012085","article-title":"Analysis of Object Detection Performance Based on Faster R-CNN","volume":"1827","author":"Li","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"104357","DOI":"10.1016\/j.compbiomed.2021.104357","article-title":"Attention-Embedded Complementary-Stream CNN for False Positive Reduction in Pulmonary Nodule Detection","volume":"133","author":"Sun","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tao, G., Zhou, F., Liu, F., Gui, H., Luo, F., Huang, W.C., Ni Zou, J., and Phoebe Chen, Y.P. (2023). DFNodule: A Novel Deformable Faster R-CNN for Lung Nodule Detection. Proceedings of the 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE.","DOI":"10.1109\/BIBM58861.2023.10385637"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"15543","DOI":"10.1038\/s41598-025-00309-7","article-title":"LN-DETR: Cross-Scale Feature Fusion and Re-Weighting for Lung Nodule Detection","volume":"15","author":"Zhou","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.3390\/make5040083","article-title":"A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS","volume":"5","author":"Terven","year":"2023","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_23","first-page":"945","article-title":"Real-Time Pulmonary Nodule Detection Algorithm Combining Attention and Multipath Fusion","volume":"44","author":"Zhao","year":"2024","journal-title":"J. Comput. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.media.2017.06.015","article-title":"Validation, Comparison, and Combination of Algorithms for Automatic Detection of Pulmonary Nodules in Computed Tomography Images: The LUNA16 Challenge","volume":"42","author":"Setio","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hussain, M. (2023). YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial Defect Detection. Machines, 11.","DOI":"10.3390\/machines11070677"},{"key":"ref_26","unstructured":"Li, C., Zhou, A., and Yao, A. (2022). Omni-Dimensional Dynamic Convolution. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","article-title":"CBAM: Convolutional Block Attention Module","volume":"Volume 11211","author":"Ferrari","year":"2018","journal-title":"Computer Vision\u2014ECCV 2018"},{"key":"ref_28","unstructured":"Tong, Z., Chen, Y., Xu, Z., and Yu, R. (2023). Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yang, L., and Honarvar Shakibaei Asli, B. (2025). MSConv-YOLO: An Improved Small Target Detection Algorithm Based on YOLOv8. J. Imaging, 11.","DOI":"10.3390\/jimaging11080285"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Park, D. (2024). A Comprehensive Review of Performance Metrics for Computer-Aided Detection Systems. Bioengineering, 11.","DOI":"10.3390\/bioengineering11111165"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/7\/329\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T09:06:46Z","timestamp":1784624806000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/7\/329"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,21]]},"references-count":30,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["jimaging12070329"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12070329","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,21]]}}}