{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T13:18:10Z","timestamp":1777727890574,"version":"3.51.4"},"reference-count":39,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T00:00:00Z","timestamp":1777420800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Accurate detection of small intestinal lesions in wireless capsule endoscopy (WCE) images remains challenging because lesions are often small, weakly contrasted, irregular in shape, and easily confused with complex mucosal backgrounds. To address these difficulties, this study proposes YOLOv12-WCIRS, a WCE-oriented improvement of YOLOv12 that jointly enhances local feature extraction, selective multi-scale fusion, background suppression, localization sensitivity, and scale-aware optimization. The proposed framework incorporates a Weighted Convolution (WConv) module, a Contextual Selection Fusion Module (CSFM), an Information Integration Attention Fusion (IIA_Fusion) module, a Receptive Field Attention-based detection head (RFAHeadDetect), and a Scale Dynamic Loss (SD Loss). Experiments on the SEE-AI dataset show that YOLOv12-WCIRS achieves 83.4% mAP@0.5 and 61.1% mAP@0.5:0.95, improving mAP@0.5 from 76.9% to 83.4% over the direct baseline YOLOv12 while maintaining competitive efficiency. Additional analyses, including cross-dataset validation on overlapping categories in Kvasir-Capsule, normal-frame false-alarm evaluation, false-positive\/false-negative breakdown, and repeated-run statistical testing, further support the robustness and practical value of the proposed framework. These results indicate that YOLOv12-WCIRS provides an effective solution for automated lesion detection in WCE images and shows promise for computer-aided capsule endoscopy analysis.<\/jats:p>","DOI":"10.3390\/computers15050283","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T10:34:42Z","timestamp":1777458882000},"page":"283","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["YOLOv12-WCIRS: An Improved YOLOv12-Based Framework for Small Intestinal Lesion Detection in WCE"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4059-1714","authenticated-orcid":false,"given":"Shiren","family":"Ye","sequence":"first","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213168, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liangjing","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213168, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zetong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213168, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haipeng","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213168, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1055\/a-1973-3796","article-title":"Small-bowel capsule endoscopy and device-assisted enteroscopy for diagnosis and treatment of small-bowel disorders: European Society of Gastrointestinal Endoscopy (ESGE) Guideline\u2014Update 2022","volume":"55","author":"Pennazio","year":"2023","journal-title":"Endoscopy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1111\/den.13896","article-title":"Artificial intelligence and deep learning for small bowel capsule endoscopy","volume":"33","author":"Trasolini","year":"2021","journal-title":"Dig. Endosc."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1016\/j.dld.2021.04.024","article-title":"The impact of reader fatigue on the accuracy of capsule endoscopy interpretation","volume":"53","author":"Beg","year":"2021","journal-title":"Dig. Liver Dis."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e2221992","DOI":"10.1001\/jamanetworkopen.2022.21992","article-title":"Development and validation of an artificial intelligence model for small bowel capsule endoscopy video review","volume":"5","author":"Xie","year":"2022","journal-title":"JAMA Netw. Open"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e345","DOI":"10.1016\/S2589-7500(24)00048-7","article-title":"AI-assisted capsule endoscopy reading in suspected small bowel bleeding: A multicentre prospective study","volume":"6","author":"Spada","year":"2024","journal-title":"Lancet Digit. Health"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102334","DOI":"10.1016\/j.clinre.2024.102334","article-title":"Automated detection of small bowel lesions based on capsule endoscopy using deep learning algorithm","volume":"48","author":"Li","year":"2024","journal-title":"Clin. Res. Hepatol. Gastroenterol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6283","DOI":"10.1007\/s00521-024-09422-6","article-title":"A review of small object detection based on deep learning","volume":"36","author":"Wei","year":"2024","journal-title":"Neural Comput. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"102802","DOI":"10.1016\/j.media.2023.102802","article-title":"Transformers in medical imaging: A survey","volume":"88","author":"Shamshad","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"103000","DOI":"10.1016\/j.media.2023.103000","article-title":"Advances in medical image analysis with vision Transformers: A comprehensive review","volume":"91","author":"Azad","year":"2024","journal-title":"Med. Image Anal."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Son, G., Eo, T., An, J., Oh, D.J., Shin, Y., Rha, H., Kim, Y.J., Lim, Y.J., and Hwang, D. (2022). Small bowel detection for wireless capsule endoscopy using convolutional neural networks with temporal filtering. Diagnostics, 12.","DOI":"10.3390\/diagnostics12081858"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"106819","DOI":"10.3748\/wjg.v31.i27.106819","article-title":"Deep learning-based localization and lesion detection in capsule endoscopy for patients with suspected small-bowel bleeding","volume":"31","author":"Kwon","year":"2025","journal-title":"World J. Gastroenterol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1111\/jgh.16369","article-title":"Comparison of clinical utility of deep learning-based systems for small-bowel capsule endoscopy reading","volume":"39","author":"Aoki","year":"2024","journal-title":"J. Gastroenterol. Hepatol."},{"key":"ref_13","unstructured":"Tian, Y., Ye, Q., and Doermann, D. (2025). YOLOv12: Attention-Centric Real-Time Object Detectors. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e258","DOI":"10.1002\/deo2.258","article-title":"Small bowel capsule endoscopy examination and open access database with artificial intelligence: The SEE-artificial intelligence project","volume":"4","author":"Yokote","year":"2024","journal-title":"DEN Open"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"201","DOI":"10.12688\/f1000research.145950.1","article-title":"Review of Deep Learning Performance in Wireless Capsule Endoscopy Images for GI Disease Classification","volume":"13","author":"Habe","year":"2024","journal-title":"F1000Research"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chen, J., Xia, K., Zhang, Z., Ding, Y., Wang, G., and Xu, X. (2024). Establishing an AI model and application for automated capsule endoscopy recognition based on convolutional neural networks (with video). BMC Gastroenterol., 24.","DOI":"10.1186\/s12876-024-03482-7"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5111","DOI":"10.3748\/wjg.v30.i48.5111","article-title":"Image detection method for multi-category lesions in wireless capsule endoscopy based on deep learning models","volume":"30","author":"Xiao","year":"2024","journal-title":"World J. Gastroenterol."},{"key":"ref_18","first-page":"2415","article-title":"Multi-Scale Feature Fusion Network Model for Wireless Capsule Endoscopic Intestinal Lesion Detection","volume":"82","author":"Ye","year":"2025","journal-title":"Comput. Mater. Contin."},{"key":"ref_19","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., and Ding, G. (2024). YOLOv10: Real-Time End-to-End Object Detection. arXiv."},{"key":"ref_20","unstructured":"Khanam, R., and Hussain, M. (2024). YOLOv11: An Overview of the Key Architectural Enhancements. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e79064","DOI":"10.2196\/79064","article-title":"YOLOv12 Algorithm-Aided Detection and Classification of Lateral Malleolar Avulsion Fracture and Subfibular Ossicle Based on CT Images: Multicenter Study","volume":"13","author":"Liu","year":"2025","journal-title":"JMIR Med. Inform."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"32546","DOI":"10.1038\/s41598-025-18997-6","article-title":"Advanced real-time detection of acute ischemic stroke using YOLOv12, YOLOv11, and YOLO-NAS: A comparative study for multi-class classification","volume":"15","author":"Khater","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"108539","DOI":"10.1016\/j.cmpb.2024.108539","article-title":"Improving real-time detection of laryngeal lesions in endoscopic images using a decoupled super-resolution enhanced YOLO","volume":"260","author":"Baldini","year":"2025","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tang, Z., Huang, Y., Hu, S., Shen, T., Meng, M., Xue, T., and Jia, Z. (Eur. J. Vasc. Endovasc. Surg., 2025). Deep Learning Application of YOLOv8 for Aortic Dissection Screening Using Non-contrast Computed Tomography, Eur. J. Vasc. Endovasc. Surg., in press.","DOI":"10.1016\/j.ejvs.2025.08.054"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"9828","DOI":"10.1038\/s41598-025-93505-4","article-title":"Exploring the impact of hyperparameter and data augmentation in YOLO V10 for accurate bone fracture detection from X-ray images","volume":"15","author":"Srinivasu","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"107830","DOI":"10.1016\/j.bspc.2025.107830","article-title":"Optimized YOLOv11 model for lung nodule detection","volume":"107","author":"Liu","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Song, X., Xie, H., Gao, T., Cheng, N., and Gou, J. (2025). Improved YOLO-Based Pulmonary Nodule Detection with Spatial-SE Attention and an Aspect Ratio Penalty. Sensors, 25.","DOI":"10.3390\/s25144245"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zeng, Q., Hu, T., Chen, Z., Zheng, J., Li, J., and Pan, Y. (2025). YOLO-ED: An efficient lung cancer detection model based on improved YOLOv8. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0330732"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"53139","DOI":"10.1007\/s11042-023-17614-w","article-title":"DSFNet: Dynamic Selection-Fusion Networks for Video Salient Object Detection","volume":"83","author":"Wang","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_30","first-page":"5601313","article-title":"MLP-Net: Multilayer Perceptron Fusion Network for Infrared Small Target Detection","volume":"63","author":"Wang","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","unstructured":"Zhang, X., Liu, C., Yang, D., Song, T., Ye, Y., Li, K., and Song, Y. (2023). RFAConv: Innovating Spatial Attention and Standard Convolutional Operation. arXiv."},{"key":"ref_32","unstructured":"Yang, J., Liu, S., Wu, J., Su, X., Hai, N., and Huang, X. (March, January 25). Pinwheel-shaped Convolution and Scale-based Dynamic Loss for Infrared Small Target Detection. Proceedings of the AAAI Conference on Artificial Intelligence, Philadelphia, PA, USA."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xu, B., Ma, Z., Su, X., He, X., and Wu, X. (2025). A Lightweight Intelligent Grading Method for Lychee Anthracnose Based on Improved YOLOv12. Front. Plant Sci., 16.","DOI":"10.3389\/fpls.2025.1688675"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"170","DOI":"10.3748\/wjg.v30.i2.170","article-title":"Automatic detection of small bowel lesions with different bleeding risks based on deep learning models","volume":"30","author":"Zhang","year":"2024","journal-title":"World J. Gastroenterol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1111\/den.14670","article-title":"Deep learning in negative small-bowel capsule endoscopy improves small-bowel lesion detection and diagnostic yield","volume":"36","author":"Choi","year":"2024","journal-title":"Dig. Endosc."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Dai, Y., Gieseke, F., Oehmcke, S., Wu, Y., and Barnard, K. (2021). Attentional Feature Fusion. Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE.","DOI":"10.1109\/WACV48630.2021.00360"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Alashrafi, L., Murad, A.A., Alshorman, O., Hossain, M.S., and Hassan, M.M. (2025). Benchmarking Lightweight YOLO Object Detectors for Real-Time Hygiene Compliance Monitoring. Sensors, 25.","DOI":"10.3390\/s25196140"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ansel, J., Yang, E., He, H., Gimelshein, N., Jain, A., Voznesensky, M., Bao, B., Bell, P., Berard, D., and Burovski, E. (2024). PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation. Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2 (ASPLOS \u201924), Association for Computing Machinery (ACM).","DOI":"10.1145\/3620665.3640366"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., and Kalinin, A.A. (2020). Albumentations: Fast and Flexible Image Augmentations. 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