{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T16:34:09Z","timestamp":1773938049921,"version":"3.50.1"},"reference-count":21,"publisher":"Fuji Technology Press Ltd.","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JACIII","J. Adv. Comput. Intell. Intell. Inform."],"published-print":{"date-parts":[[2026,3,20]]},"abstract":"<jats:p>As a receptor of breast cancer prognosis and treatment, human epidermal growth factor receptor 2 (HER-2) is closely associated with breast cancer occurrence and progression. Breast cancer lesions are characterized by irregular shapes, small lesion targets, and possible multi-target lesions with overlapping boundaries. Existing algorithms have partly improved the detection accuracy of breast cancer detection model. However, it still suffers from the issues of insufficient detection accuracy and slow detection speed of small and multi-target lesions and requires a huge mass of sample data for iteration during model training, which is demanding on datasets. To address this problem, an improved model is proposed in this paper based on YOLOv10. The model introduces VanillaNet, a lightweight backbone network that can significantly improve detection accuracy by reducing the network depth of the model to equalize detection speed and performance. In addition, the RefConv module is embedded into the C2f structure to further reduce channel redundancy and smooth out lossy situations. In the feature fusion network part, the introduction of a lightweight up-sampling operator content-aware feature reorganization CARAFE module enhances the quality and richness of output features, which effectively improves detection accuracy and speed. The accuracy of the improved model is 98.8%. Thus, the improved model is a significant advantage over mainstream models such as traditional faster region-based convolutional neural network (RCNN), YOLOv5, YOLOv7, YOLOv8, YOLOv10, and YOLOv12.<\/jats:p>","DOI":"10.20965\/jaciii.2026.p0388","type":"journal-article","created":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T15:02:06Z","timestamp":1773932526000},"page":"388-396","source":"Crossref","is-referenced-by-count":0,"title":["Vanilla-YOLO: A Lightweight Algorithm for Breast Cancer Detection"],"prefix":"10.20965","volume":"30","author":[{"given":"Shu-Hua","family":"Li","sequence":"first","affiliation":[{"name":"School of Graduate Studies, Mapua University, 658 Muralla Street, Intramuros, Manila 1002, Philippines"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mary Jane C.","family":"Samonte","sequence":"additional","affiliation":[{"name":"School of Graduate Studies, Mapua University, 658 Muralla Street, Intramuros, Manila 1002, Philippines"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng-Long","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Computer and Software, Dalian Neusoft University of Information, 8 Software Park Road, Dalian 116023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"8550","published-online":{"date-parts":[[2026,3,20]]},"reference":[{"key":"key-10.20965\/jaciii.2026.p0388-1","doi-asserted-by":"crossref","unstructured":"D. Xavier, I. Miyawaki, C. A. C. Jorge et al., \u201cArtificial intelligence for triaging of breast cancer screening mammograms and workload reduction: A meta-analysis of a deep learning software,\u201d J. of Medical Screening, Vol.31, No.3, pp. 157-165, 2024. https:\/\/doi.org\/10.1177\/09691413231219952","DOI":"10.1177\/09691413231219952"},{"key":"key-10.20965\/jaciii.2026.p0388-2","doi-asserted-by":"crossref","unstructured":"H. S. Rugo et al., \u201c185O Trastuzumab deruxtecan (T-DXd) vs treatment of physician\u2019s choice (TPC) in patients (pts) with HER2-low unresectable and\/or metastatic breast cancer (mBC): A detailed safety analysis of the randomized, phase III DESTINY-Breast04 trial,\u201d ESMO Open, Vol.8, No.1, Article No.101374, 2023. https:\/\/doi.org\/10.1016\/j.esmoop.2023.101374","DOI":"10.1016\/j.esmoop.2023.101374"},{"key":"key-10.20965\/jaciii.2026.p0388-3","doi-asserted-by":"crossref","unstructured":"Y. Yang et al., \u201cA new nomogram for predicting the malignant diagnosis of breast imaging reporting and data system (BI-RADS) ultrasonography category 4A lesions in women with dense breast tissue in the diagnostic setting,\u201d Quant. Imaging Med. Surg., Vol.11, No.7, pp. 3005-3017, 2021. https:\/\/doi.org\/10.21037\/qims-20-1203","DOI":"10.21037\/qims-20-1203"},{"key":"key-10.20965\/jaciii.2026.p0388-4","unstructured":"S. Ren et al., \u201cFaster R-CNN: Towards real-time object detection with region proposal networks,\u201d Proc. of the 29th Int. Conf. on Neural Information Processing Systems, pp. 91-99, 2015."},{"key":"key-10.20965\/jaciii.2026.p0388-5","doi-asserted-by":"crossref","unstructured":"J. Xu, H. Ren, S. Cai, and X. 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Liao, \u201cYOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,\u201d 2023 IEEE\/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 7464-7475, 2023. https:\/\/doi.org\/10.1109\/CVPR52729.2023.00721","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"key-10.20965\/jaciii.2026.p0388-8","unstructured":"Y. Tian, Q. Ye, and D. Doermann, \u201cYOLOv12: Attention-Centric Real-Time Object Detector,\u201d arXiv:2502.12524, 2025. https:\/\/doi.org\/10.48550\/arXiv.2502.12524"},{"key":"key-10.20965\/jaciii.2026.p0388-9","unstructured":"M. Lei, S. Li, Y. Wu, H. Hu, Y. Zhou, X. Zheng, G. Ding, S. Du, Z. Wu, and Y. Gao, \u201cYOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception,\u201d arXiv:2506.17733, 2025. https:\/\/doi.org\/10.48550\/arXiv.2506.17733"},{"key":"key-10.20965\/jaciii.2026.p0388-10","doi-asserted-by":"crossref","unstructured":"L. Sun, Y. Zhang, T. Liu et al., \u201cA collaborative multi-task learning method for BI-RADS category 4 breast lesion segmentation and classification of MRI images,\u201d Computer Methods and Programs in Biomedicine, Vol.240, Article No.107705, 2023. https:\/\/doi.org\/10.1016\/j.cmpb.2023.107705","DOI":"10.1016\/j.cmpb.2023.107705"},{"key":"key-10.20965\/jaciii.2026.p0388-11","doi-asserted-by":"crossref","unstructured":"F. Rajeena P.P and S. Tehsin, \u201cA Framework for Breast Cancer Classification with Deep Features and Modified Grey Wolf Optimization,\u201d Mathematics, Vol.13, No.8, Article No.1236, 2025. https:\/\/doi.org\/10.3390\/math13081236","DOI":"10.3390\/math13081236"},{"key":"key-10.20965\/jaciii.2026.p0388-12","doi-asserted-by":"crossref","unstructured":"M. Epimack, M. He, and M. Palme, \u201cDeep learning mammography classification with a small set of data,\u201d Current Medical Imaging, Vol.20, Article No.e110823219688, 2024. https:\/\/doi.org\/10.2174\/1573405620666230811142718","DOI":"10.2174\/1573405620666230811142718"},{"key":"key-10.20965\/jaciii.2026.p0388-13","unstructured":"T. Yang, L. Yang, M. Yang et al., \u201cStudy on the application of YOLO algorithm based on improved YOLO network in the detection of ultrasound image for breast tumor,\u201d China Medical Equipment, Vol.21, No.9, pp. 23-27, 2024."},{"key":"key-10.20965\/jaciii.2026.p0388-14","unstructured":"A. Alaa and S. A. Aly, \u201cAcute lymphoblastic leukemia diagnosis employing YOLOv11, YOLOv8, ResNet50, and inception-ResNet-v2 deep learning models,\u201d arXiv:2502.09804v1, 2025. https:\/\/doi.org\/10.48550\/arXiv.2502.09804"},{"key":"key-10.20965\/jaciii.2026.p0388-15","unstructured":"A. Wang, H. Chen, L. H. Liu et al., \u201cYOLOv10: Real time end-to-end object detection,\u201d arXiv:2405.14458, 2024. https:\/\/doi.org\/10.48550\/arXiv.2405.14458"},{"key":"key-10.20965\/jaciii.2026.p0388-16","doi-asserted-by":"crossref","unstructured":"J. Terven, D.-M. C\u00f3rdova-Esparza, J.-A. Romero-Gonz\u00e1lez et al., \u201cA comprehensive review of YOLO architectures in computer vision: From YOLOv1 to YOLOv8 and YOLO-NAS,\u201d Machine Learning and Knowledge Extraction, Vol.5, No.4, pp. 1680-1716, 2023. https:\/\/doi.org\/10.3390\/make5040083","DOI":"10.3390\/make5040083"},{"key":"key-10.20965\/jaciii.2026.p0388-17","doi-asserted-by":"crossref","unstructured":"H. Chen, Y. Wang, J. Guo, and D. Tao, \u201cVanillaNet: The power of minimalism in deep learning,\u201d arXiv:2305.12972, 2023. https:\/\/doi.org\/10.48550\/arXiv.2305.12972","DOI":"10.52202\/075280-0308"},{"key":"key-10.20965\/jaciii.2026.p0388-18","unstructured":"M. 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