{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T16:07:56Z","timestamp":1782317276046,"version":"3.54.5"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T00:00:00Z","timestamp":1769126400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Breast cancer, one of the most life-threatening diseases that commonly affects women, can be effectively diagnosed using breast ultrasound imaging. A hybrid deep learning based ensemble framework combining the effectiveness of different convolutional neural network models has been proposed for breast ultrasound image classification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Three distinct deep learning models, namely, EffcientNetB7, DenseNet121, and ConvNeXtTiny, have been independently trained on breast ultrasound image datasets in parallel to capture complementary representations. Local features are extracted using EffcientNetB7 through depthwise separable convolutions, whereas structural details are preserved by DenseNet121 utilizing dense connectivity. Global spatial relationships are modeled using ConvNeXtTiny via large kernel operations. Diverse local, global, and hierarchical features extracted with respect to multiple perspectives are integrated into a high-dimensional unified representation from which non-linear decision boundaries are learned utilizing XGBoost as the feature fusion classifier. Additionally, a soft voting ensemble method averages the predicted probabilities of the individual convolutional network architectures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The model was evaluated using the BUSI dataset, the BUS-UCLM dataset, and the UDIAT dataset. The accuracy, precision, recall, F1 score, and AUC values obtained on the BUSI data set are 88.46%, 88.49%, 88.46%, 88.45%, and 95.38%, respectively. On the BUS-UCLM dataset, the corresponding values are 90. 51%, 90. 56%, 90. 51%, 90. 51%, and 96. 23%, respectively. The accuracy, precision, recall, F1 score, and AUC values obtained on the UDIAT dataset are 96.97%, 100.00%, 90.91%, 95.24%, and 99.17%, respectively. The decision-making capability of the model has been highlighted using SHAP and Grad-CAM visualizations, further improving the interpretability and transparency of the model, and making it more robust for breast ultrasound image classification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The HED-Net framework exhibits significant potential for clinical application by enhancing diagnostic accuracy and decreasing interpretation time, particularly in resource-limited environments where expert radiologists are in short supply.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/frai.2025.1672488","type":"journal-article","created":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T06:46:15Z","timestamp":1769150775000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["HED-Net: a hybrid ensemble deep learning framework for breast ultrasound image classification"],"prefix":"10.3389","volume":"8","author":[{"given":"Soumya Sara","family":"Koshy","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"L. Jani","family":"Anbarasi","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Modigari","family":"Narendra","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rabindra Kumar","family":"Singh","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,23]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"5742","DOI":"10.1109\/JBHI.2024.3396130","article-title":"Robust epileptic seizure detection based on biomedical signals using an advanced multi-view deep feature learning approach","volume":"28","author":"Ahmad","year":"2024","journal-title":"IEEE J. Biomed. Health Informat"},{"key":"B2","doi-asserted-by":"publisher","first-page":"8141530","DOI":"10.1155\/2022\/8141530","article-title":"A novel hybrid deep learning model for metastatic cancer detection","volume":"2022","author":"Ahmad","year":"2022","journal-title":"Comput. Intellig. Neurosci"},{"key":"B3","doi-asserted-by":"publisher","first-page":"104863","DOI":"10.1016\/j.dib.2019.104863","article-title":"Dataset of breast ultrasound images","volume":"28","author":"Al-Dhabyani","year":"2020","journal-title":"Data Brief"},{"key":"B4","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1117\/12.2612538","article-title":"\u201cKing Abdullah International Medical Research Center (KAIMRC)'s breast cancer big images data set,\u201d","volume":"12037","author":"Almazroa","year":"2022","journal-title":"Proc. SPIE Med. Imag.: Imag. Informat. Healthcare, Res"},{"key":"B5","doi-asserted-by":"publisher","first-page":"e22406","DOI":"10.1016\/j.heliyon.2023.e22406","article-title":"Breast cancer classification based on convolutional neural network and image fusion approaches using ultrasound images","volume":"9","author":"Alotaibi","year":"2023","journal-title":"Heliyon"},{"key":"B6","doi-asserted-by":"publisher","first-page":"135","DOI":"10.3390\/diagnostics12010135","article-title":"A novel multistage transfer learning for ultrasound breast cancer image classification","volume":"12","author":"Ayana","year":"2022","journal-title":"Diagnostics"},{"key":"B7","doi-asserted-by":"publisher","first-page":"1290","DOI":"10.3390\/bioengineering11121290","article-title":"An intelligent approach for early and accurate predication of cardiac disease using hybrid artificial intelligence techniques","volume":"11","author":"Bilal","year":"2024","journal-title":"Bioengineering"},{"key":"B8","doi-asserted-by":"publisher","first-page":"105018","DOI":"10.1016\/j.imavis.2024.105018","article-title":"Deep learning and genetic algorithm-based ensemble model for feature selection and classification of breast ultrasound images","volume":"146","author":"Dar","year":"2024","journal-title":"Image Vis. Comput"},{"key":"B9","doi-asserted-by":"publisher","first-page":"104871","DOI":"10.1016\/j.bspc.2023.104871","article-title":"Breast ultrasound image classification using fuzzy-rank-based ensemble network","volume":"85","author":"Deb","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"B10","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1007\/978-981-19-9819-5_37","article-title":"\u201cA novel transfer learning-based model for ultrasound breast cancer image classification,\u201d","author":"Gupta","year":"2023","journal-title":"Proc. Comput. Vis. Bio-Inspired Comput"},{"key":"B11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.22581\/muet1982.0035","article-title":"Deep learning-based dual optimization framework for accurate thyroid disease diagnosis using CNN architectures","volume":"44","author":"Haider","year":"2025","journal-title":"Mehran Univ. Res. J. Eng. Technol"},{"key":"B12","doi-asserted-by":"publisher","first-page":"200367","DOI":"10.1016\/j.iswa.2024.200367","article-title":"A robust encoder decoder based weighted segmentation and dual staged feature fusion based meta classification for breast cancer utilizing ultrasound imaging","volume":"22","author":"Himel","year":"","journal-title":"Intelligent Syst. Applic"},{"key":"B13","doi-asserted-by":"publisher","first-page":"00486","DOI":"10.1016\/j.array.2025.100486","article-title":"IsharaNet: a robust nested feature fusion coupled with attention incorporated width scaled lightweight architecture for Bengali sign language recognition","volume":"127","author":"Himel","year":"2025","journal-title":"Array"},{"key":"B14","doi-asserted-by":"publisher","first-page":"54758","DOI":"10.1109\/ACCESS.2024.3388715","article-title":"Feature fusion based ensemble of deep networks for acute leukemia diagnosis using microscopic smear images","volume":"12","author":"Himel","year":"","journal-title":"IEEE Access"},{"key":"B15","doi-asserted-by":"crossref","first-page":"4700","DOI":"10.1109\/CVPR.2017.243","article-title":"\u201cDensely connected convolutional networks,\u201d","volume-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Huang","year":"2017"},{"key":"B16","doi-asserted-by":"publisher","first-page":"100555","DOI":"10.1016\/j.mlwa.2024.100555","article-title":"Enhancing breast cancer segmentation and classification: an ensemble deep convolutional neural network and U-net approach on ultrasound images","volume":"16","author":"Islam","year":"2024","journal-title":"Mach. Learn. Appl"},{"key":"B17","doi-asserted-by":"publisher","first-page":"807","DOI":"10.3390\/s22030807","article-title":"Breast cancer classification from ultrasound images using probability-based optimal deep learning feature fusion","volume":"22","author":"Jabeen","year":"2022","journal-title":"Sensors"},{"key":"B18","doi-asserted-by":"publisher","first-page":"1859","DOI":"10.3390\/diagnostics11101859","article-title":"Classification of breast cancer lesions in ultrasound images by using attention layer and loss ensemble in deep convolutional neural networks","volume":"11","author":"Kalafi","year":"2021","journal-title":"Diagnostics"},{"key":"B19","doi-asserted-by":"publisher","first-page":"106569","DOI":"10.5306\/wjco.v16.i5.106569","article-title":"Impact of the family and socioeconomic factors as a tool of prevention of breast cancer","volume":"16","author":"Karmakar","year":"2025","journal-title":"World J. Clin. Oncol"},{"key":"B20","first-page":"943","article-title":"Clinical prediction of female infertility through advanced machine learning techniques","volume":"6","author":"Khan","year":"2024","journal-title":"Int. J. Innov. Sci. Technol"},{"key":"B21","doi-asserted-by":"publisher","first-page":"1060","DOI":"10.3390\/bioengineering11111060","article-title":"Systematic meta-analysis of computer-aided detection of breast cancer using hyperspectral imaging","volume":"11","author":"Karmakar","year":"2024","journal-title":"Bioengineering"},{"key":"B22","doi-asserted-by":"publisher","first-page":"100154","DOI":"10.1016\/j.mlwa.2021.100154","article-title":"An investigation of XGBoost-based algorithm for breast cancer classification","volume":"6","author":"Liew","year":"2021","journal-title":"Mach. Learn. Appl"},{"key":"B23","first-page":"614","article-title":"\u201cA new dataset and a baseline model for breast lesion detection in ultrasound videos,\u201d","volume-title":"Medical Image Computing and Computer Assisted Intervention","author":"Lin","year":"2022"},{"key":"B24","first-page":"11976","article-title":"\u201cA ConvNet for the 2020s,\u201d","volume-title":"2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Liu","year":"2022"},{"key":"B25","doi-asserted-by":"publisher","first-page":"4765","DOI":"10.48550\/arXiv.1705.07874","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst"},{"key":"B26","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1159\/000297775","article-title":"Role of breast ultrasound for the detection and differentiation of breast lesions","volume":"5","author":"Madjar","year":"2010","journal-title":"Breast Care"},{"key":"B27","doi-asserted-by":"publisher","first-page":"2354","DOI":"10.3390\/math12152354","article-title":"An interpretable breast ultrasound image classification algorithm based on convolutional neural network and transformer","volume":"12","author":"Meng","year":"2024","journal-title":"Mathematics"},{"key":"B28","doi-asserted-by":"publisher","first-page":"105361","DOI":"10.1016\/j.cmpb.2020.105361","article-title":"Computer-aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks","volume":"190","author":"Moon","year":"2020","journal-title":"Comput. Methods Programs Biomed"},{"key":"B29","first-page":"148","article-title":"\u201cVision mamba for classification of breast ultrasound images,\u201d","volume-title":"Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care","author":"Nasiri-Sarvi","year":"2024"},{"key":"B30","doi-asserted-by":"publisher","first-page":"262","DOI":"10.3390\/forecast4010015","article-title":"Explainable ensemble machine learning for breast cancer diagnosis based on ultrasound image texture features","volume":"4","author":"Rezazadeh","year":"2022","journal-title":"Forecasting"},{"key":"B31","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1109\/ICCV.2017.74","article-title":"\u201cGrad-CAM: visual explanations from deep networks via gradient-based localization,\u201d","volume-title":"2017 IEEE International Conference on Computer Vision (ICCV)","author":"Selvaraju","year":"2017"},{"key":"B32","first-page":"6105","article-title":"\u201cEfficientNet: Rethinking model scaling for convolutional neural networks,\u201d","volume-title":"Proceedings of the 36th International Conference on Machine Learning, Long Beach, California, PMLR 97","author":"Tan","year":"2019"},{"key":"B33","doi-asserted-by":"publisher","first-page":"9025470","DOI":"10.1155\/2021\/9025470","article-title":"Deep learning in cancer diagnosis and prognosis prediction: a minireview on challenges, recent trends, future directions","volume":"2021","author":"Tufail","year":"2021","journal-title":"Comput. Math. Methods Med"},{"key":"B34","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1038\/s41597-025-04562-3","article-title":"BUS-UCLM: breast ultrasound lesion segmentation dataset","volume":"12","author":"Vallez","year":"2025","journal-title":"Sci. Data"},{"key":"B35","doi-asserted-by":"publisher","first-page":"5057","DOI":"10.3390\/curroncol31090374","article-title":"A novel deep learning model for breast tumor ultrasound image classification with lesion region perception","volume":"31","author":"Wei","year":"2024","journal-title":"Curr. Oncol"},{"key":"B36","doi-asserted-by":"publisher","first-page":"763","DOI":"10.5220\/0012377800003657","article-title":"\u201cResNet-101 empowered deep learning for breast cancer ultrasound image classification,\u201d","author":"Yadav","year":"2024","journal-title":"Proc. Int. Joint Conf. Biomed. Eng. Syst. Technol"},{"key":"B37","doi-asserted-by":"publisher","first-page":"1218","DOI":"10.1109\/JBHI.2017.2731873","article-title":"Automated breast ultrasound lesions detection using convolutional neural networks","volume":"22","author":"Yap","year":"2017","journal-title":"IEEE J. Biomed. Health Informat"}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2025.1672488\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T06:46:17Z","timestamp":1769150777000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2025.1672488\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,23]]},"references-count":37,"alternative-id":["10.3389\/frai.2025.1672488"],"URL":"https:\/\/doi.org\/10.3389\/frai.2025.1672488","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,23]]},"article-number":"1672488"}}