Quantum-infused deep learning for dual-task thyroid ultrasound diagnostics

© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )

Download PDF

XML

Cite

Abstract

Accurate classification of thyroid nodules is essential for early intervention and improved clinical outcomes; however, its diagnostic performance remains limited by inter-observer variability, limited data availability, and class imbalance in ultrasound imaging. Although existing deep learning models have demonstrated strong performance, they often struggle to capture both global semantic context and fine-grained structural details under limited data. This study introduces a hybrid quantum– classical deep learning framework that leverages quantum variational filtering in tandem with transfer learning via ViT-B16 and EfficientNet-B3 to enhance feature representation. A multi-stage preprocessing pipeline was designed to improve input diversity and class balance. The model jointly performs binary tumor classification and Thyroid Imaging Reporting and Data System (TI-RADS) level prediction, achieving 94.2% accuracy for benign-versus-malignant tumor classification and 91.8% macro-accuracy for TI-RADS prediction. The proposed approach demonstrates promising robustness and generalization in limited-data environments, highlighting the potential of integrating quantum encoding with classical deep learning architectures for medical image diagnostics.

Graphical abstract

Keywords

Thyroid nodule classification

TI-RADS prediction

Quantum variational filtering

Feature fusion

Patch-wise feature learning

Funding

This work was made possible by the Visvesvaraya PhD Scheme scholarship.

Conflict of interest

The authors declare no competing interests.

References

Mnatsakanian A, Al-Khalili Y. Anatomy, Head and Neck, Thyroid muscles. In: StatPearls. Treasure Island, FL, USA: StatPearls Publishing; 2023.
Tessler FN, Middleton WD, Grant EG, et al. ACR Thyroid Imaging, Reporting and Data System (TI-RADS): White Paper of the ACR TI-RADS Committee. J Am Coll Radiol. 2017;14(5):587-595. doi: 10.1016/j.jacr.2017.01.046
Tessler FN, Middleton WD, Grant EG. Thyroid Imaging Reporting and Data System (TI-RADS): A User’s Guide. Radiology. 2018;287(1):29-36. doi: 10.1148/radiol.2017171240
Lixandru-Petre IO, Dima A, Musat M, et al. Machine Learning for Thyroid Cancer Detection, Presence of Metastasis, and Recurrence Predictions—A Scoping Review. Cancers. 2025;17(8):1308. doi: 10.3390/cancers17081308
Ha EJ, Lee JH, Mak N, et al. A Deep Learning-Based Artificial Intelligence Model Assisting Thyroid Nodule Diagnosis and Management: Pilot Results for Evaluating Thyroid Malignancy in Pediatric Cohorts. Thyroid. 2025;35(6):652-661. doi: 10.1089/thy.2024.0627
Fontana E, Rudolph MS, Duncan R, Rungger I, Cîrstoiu C. Classical simulations of noisy variational quantum circuits. npj Quantum Inf. 2025;11(1):84. doi: 10.1038/s41534-024-00955-1
Liu CY, Chen SYC, Chen KC, Huang WJ, Chang YJ. Programming Variational Quantum Circuits with Quantum-Train Agent. In: Proceedings of the 2025 International Conference on Quantum Communications, Networking, and Computing (QCNC). New York, NY, USA: IEEE; 2025:544-548. doi: 10.1109/qcnc64685.2025.00091
Cao CL, Li QL, Tong J, et al. Artificial intelligence in thyroid ultrasound. Front Oncol. 2023;13:1060702. doi: 10.3389/fonc.2023.1060702
Khan T. Application of Two-Class Neural Network-based classification model to predict the onset of Thyroid Disease. In: Proceedings of the 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence). New York, NY, USA: IEEE; 2021:114-118. doi: 10.1109/confluence51648.2021.9377097
Srivastava R, Kumar P. Optimizing CNN based model for thyroid nodule classification using data augmen- tation, segmentation and boundary detection techniques. Multimed Tools Appl. 2023;82(26):41037-41072. doi: 10.1007/s11042-023-15068-8
Sun J, Wu B, Zhao T, et al. Classification for thyroid nodule using ViT with contrastive learning in ultrasound images. Comput Biol Med. 2023;152:106444. doi: 10.1016/j.compbiomed.2022.106444
Wang Y, Gan J. Benign and Malignant Classification of Thyroid Nodules Based on ConvNeXt. In: Proceedings of the 2022 3rd International Conference on Control, Robotics and Intelligent System. New York, NY, USA: Association for Computing Machinery; 2022:56-60. doi: 10.1145/3562007.3562017
Zhang J, Zhang H, Jiang P, et al. High efficiency classification of thyroid cytopathological images based on knowledge distillation and vision transformer. Sci Rep. 2025;15(1):30059. doi: 10.1038/s41598-025-15728-9
Zheng Z, Su T, Wang Y, et al. A novel ultrasound image diagnostic method for thyroid nodules. Sci Rep. 2023;13(1):1654. doi: 10.1038/s41598-023-28932-2
Aboudi N, Khachnaoui H, Moussa O, Khlifa N. Bilinear Pooling for Thyroid Nodule Classification in Ultrasound Imaging. Arab J Sci Eng. 2023;48(8):10563-10573. doi: 10.1007/s13369-023-07674-3
Gong H, Chen J, Chen G, Li H, Li G, Chen F. Thyroid region prior guided attention for ultrasound segmentation of thyroid nodules. Comput Biol Med. 2023;155:106389. doi: 10.1016/j.compbiomed.2022.106389
Xiao X, Zhou Y, Zhu Y, Li Y, Qi T, Wang W. A multimodal prompt-tuning method of ultrasound diagnosis for thyroid nodule. Front Med. 2025;12:1686374. doi: 10.3389/fmed.2025.1686374
Fang M, Ma Q, Xu B. Automated multi-class classification of thyroid nodules in ultrasound imaging using transformer-based segmentation and hybrid feature learning. J Radiat Res Appl Sci. 2025;18(4):101782. doi: 10.1016/j.jrras.2025.101782
Zhou YT, Yang TY, Han XH, Piao JC. Thyroid-DETR: Thyroid nodule detection model with transformer in ultrasound images. Biomed Signal Processing Control. 2024;98:106762. doi: 10.1016/j.bspc.2024.106762
Liu Q, Li Y, Hao Y, et al. Multimodal ultrasound multistage classification of PTC cervical lymph node metastasis via DualSwinThyroid. Front Oncol. 2024;14:1349388. doi: 10.3389/fonc.2024.1349388
Rashed A, Medhat T, Elgarayhi A. Enhancing automatic diagnosis of thyroid nodules from ultrasound scans leveraging deep learning models. Sci Rep. 2025;15(1):40364. doi: 10.1038/s41598-025-25780-0
Chi J, Chen J hui, Wu B, Zhao J, Wang K, Yu X, et al. A Dual-Branch Cross-Modality-Attention Network for Thyroid Nodule Diagnosis Based on Ultrasound Images and Contrast-Enhanced Ultrasound Videos. IEEE J Biomed Health Inform. 2025;29(2):1269-1282. doi: 10.1109/jbhi.2024.3472609
Jiang S, Zhang N, Li C, Tong L, Yang X. Application of deep learning based on convolutional neural network model in multimodal ultrasound diagnosis of unexplained cervical lymph node enlargement. Front Oncol. 2025;15:1542265. doi: 10.3389/fonc.2025.1542265
Shen P, Yang Z, Sun J, et al. Explainable multimodal deep learning for predicting thyroid cancer lateral lymph node metastasis using ultrasound imaging. Nat Commun. 2025;16(1):7052. doi: 10.1038/s41467-025-62042-z
Maheshwari D, Ullah U, Marulanda PAO, et al. Quantum Machine Learning Applied to Electronic Healthcare Records for Ischemic Heart Disease Classification. Hum Cent Comput Inf Sci. 2023;13(06):1-8. doi: 10.22967/HCIS.2023.13.006
Sajeev V, Hima Vyshnavi AM, Krishnan Namboori PK. Thyroid Cancer Prediction Using Gene Expression Profile, Pharmacogenomic Variants And Quantum Image Processing In Deep Learning Platform-A Theranostic Approach. In: Proceedings of the 2020 International Conference for Emerging Technology (INCET). New York, NY, USA: IEEE; 2020:1-5. doi: 10.1109/incet49848.2020.9154041
Gomes Ataide EJ, Ponugoti N, Illanes A, Schenke S, Kreissl M, Friebe M. Thyroid Nodule Classification for Physician Decision Support Using Machine Learning- Evaluated Geometric and Morphological Features. Sensors. 2020;20(21):6110. doi: 10.3390/s20216110
Liu T, Xie S, Zhang Y, Yu J, Niu L, Sun W. Feature selection and thyroid nodule classification using transfer learning. In: Proceeding of the 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). New York, NY, USA: IEEE; 2017:1096-1099. doi: 10.1109/isbi.2017.7950707
Kwon SW, Choi IJ, Kang JY, Jang WI, Lee GH, Lee MC. Ultrasonographic Thyroid Nodule Classification Using a Deep Convolutional Neural Network with Surgical Pathology. J Digit Imaging. 2020;33(5):1202-1208. doi: 10.1007/s10278-020-00362-w
Zhu Y, Fu Z, Fei J. An image augmentation method using convolutional network for thyroid nodule classification by transfer learning. In: Proceedings of the 2017 3rd IEEE International Conference on Computer and Communications (ICCC). New York, NY, USA: IEEE; 2017:1819-1823. doi: 10.1109/compcomm.2017.8322853
Hang Y. Thyroid Nodule Classification in Ultrasound Images by Fusion of Conventional Features and Res-GAN Deep Features. Singh D, ed. J Healthc Eng. 2021;2021:1-7. doi: 10.1155/2021/9917538
Pedraza L, Vargas C, Narváez F, Durán O, Muñoz E, Romero E. An open access thyroid ultrasound image database. In: Romero E, Lepore N, eds. Proceedings of the SPIE 9287,10^thInternational Symposium on Medical Information Processing and Analysis, 92870W. SPIE; 2015. doi: 10.1117/12.2073532
Xu C, Sun Y, Zhang Y, et al. Stain Normalization of Histopathological Images Based on Deep Learning: A Review. Diagnostics. 2025;15(8):1032. doi: 10.3390/diagnostics15081032
Kim YS, Kim MK, Fu N, Liu J, Wang J, Srebric J. Investigating the impact of data normalization methods on predicting electricity consumption in a building using different artificial neural network models. Sustain Cities Soc. 2025;118:105570. doi: 10.1016/j.scs.2024.105570
Liaw LCM, Tan SC, Goh PY, Lim CP. A histogram SMOTE-based sampling algorithm with incremental learning for imbalanced data classification. Inf Sci. 2025;686:121193. doi: 10.1016/j.ins.2024.121193
Hou M, Wu Y, Zhang S. Two-Party Quantum Private Comparison with Pauli Operators. Axioms. 2025;14(8):549. doi: 10.3390/axioms14080549
Hirano Y, Fujii K. Locality-Aware Pauli-Based Computation for Local Magic State Preparation. In: Proceedings of the 2025 IEEE International Conference on Quantum Computing and Engineering (QCE). New York, NY, USA: IEEE; 2025:670-680. doi: 10.1109/qce65121.2025.00078
Pavithra KC, Kumar P, Geetha M, et al. Transformer-Based DME Classification Using Retinal OCT Images Without Data Augmentation: An Evaluation of ViT-B16 and ViT-B32 With Optimizer Impact. IEEE Access. 2025;13:180781- 180798. doi: 10.1109/access.2025.3620945
Younis EM, Ibrahim IA, Mahmoud MN, Albarrak AM. Hybrid of VGG-16 and FTVT-b16 Models to Enhance Brain Tumors Classification Using MRI Images. Diagnostics. 2025;15(16):2014. doi: 10.3390/diagnostics15162014
Navaneethakrishnan M, Robinson Joel M, Kalavai Palani S, Gnanaprakasam GJ. EfficientNet-deep quantum neural network-based economic denial of sustainability attack detection to enhance network security in cloud. Netw Comput Neural Syst. 2024;36(4):1360-1384. doi: 10.1080/0954898x.2024.2361093
Altaf AR, Altaf A, Rehman FU. Modified EfficientNet-B0 Architecture Optimized with Quantum-Behaved Algorithm for Skin Cancer Lesion Assessment. Diagnostics. 2025;15(24):3245. doi: 10.3390/diagnostics15243245

Previous article in this issue

Next article in this issue

Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing