Skip to main content

Advertisement

Springer Nature Link
Log in

Quantum neural network-assisted learning for small medical datasets: a case study in emphysema detection

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Advancement in AI and deep learning has transformed medical image diagnosis; however, approaches to disease diagnosis, such as the detection of emphysema, one of the severest forms of COPD, stand to benefit from these technologies more. The main problem with the detection of emphysema from CT scans is the lack of very large, annotated datasets to train deep learning models. Classic models, like CNNs, are usually underfitting to small datasets and hence have very poor diagnostic accuracy. To address this challenge, we consider an integrated hybrid quantum–classical neural network model by combining the quantum variational circuits with CNNs. This new approach uses the power of quantum computing to identify subtle patterns in small datasets and could solve one of the key problems of deep learning. The model is pre-trained on large chest X-ray datasets and fine-tuned on a smaller emphysema dataset, which allows it to generalize more when data is limited. The experimental results confirm that the proposed approach is effective; namely, the quantum-assisted model reaches an accuracy of 0.5690 and F1-score of 0.5990, outperforming the traditional CNN models. This work points to the novelty of quantum computing in diagnosis with limited amounts of data, a very important challenge in this area of medical AI. Given that our research will conform to the limitation and work on small datasets, this work opens a new frontier in medical image analysis and shows ways in which QNN can substantially outperform traditional methods in detecting subtle markers of diseases; this indeed contributes to the growing body of knowledge in quantum-enhanced AI and opens up new frontiers toward some potential applications in the field of diagnosis of rare diseases and health diagnostics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Data Availability

Data sets that use in paper are public dataset as follows: 1 https://www.kaggle.com/datasets/paultimothymooney/ chest-xray-pneumonia 2 https://lauge-soerensen.github.io/emphysema-database/.

References

  1. Mall PK, Singh PK, Srivastav S, Narayan V, Paprzycki M, Jaworska T, Ganzha M (2023) A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities. Healthcare Analytics, page 100216

  2. Houshmand M, Khorrampanah M, Alkhudhari AHM (2024) Optimized quantum computing technique to encrypt medical images. Opt Quant Electron 56(3):442

    Article  Google Scholar 

  3. Varoquaux G, Cheplygina V (2022) Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ Digital Med 5(1):48

    Article  Google Scholar 

  4. Davila A, Colan J, Hasegawa Y (2024) Comparison of fine-tuning strategies for transfer learning in medical image classification. Image Vis Comput 146:105012

    Article  Google Scholar 

  5. Ali H, Wang M, Xie J (2024) Cil-net: Densely connected context information learning network for boosting thyroid nodule segmentation using ultrasound images. Cogn Comput 16(3):1176–1197

    Article  Google Scholar 

  6. Zhong Y, Liu Y, Gao E, Wei C, Wang Z, Yan C (2024) Deep learning solutions for pneumonia detection: Performance comparison of custom and transfer learning models. medRxiv, pp 2024–06,

  7. Ali H, Wang M, Xie J (2024) Emtl-net: Boosting segmentation quality in histopathology images of gland and nuclei by explainable multitask learning network as an optimized strategy. Eng Sci Technol Intern J 51:101636

    Google Scholar 

  8. Ali H, Haq IU, Cui L, Feng J (2022) Msal-net: improve accurate segmentation of nuclei in histopathology images by multiscale attention learning network. BMC Med Inform Decis Mak 22(1):90

    Article  Google Scholar 

  9. Kshatri SS, Singh D (2023) Convolutional neural network in medical image analysis: A review. Archiv Comput Methods Eng 30(4):2793–2810

    Article  Google Scholar 

  10. Salehi AW, Khan S, Gupta G, Alabduallah BI, Almjally A, Alsolai H, Siddiqui T, Mellit A (2023) A study of cnn and transfer learning in medical imaging: Advantages, challenges, future scope. Sustainability 15(7):5930

    Article  Google Scholar 

  11. Sistaninejhad B, Rasi H, Nayeri P (2023) A review paper about deep learning for medical image analysis. Comput Math Methods Med 2023(1):7091301

    Article  Google Scholar 

  12. Li M, Jiang Y, Zhang Y, Zhu H (2023) Medical image analysis using deep learning algorithms. Front Public Health 11:1273253

    Article  Google Scholar 

  13. Gaur L, Bhatia U, Jhanjhi N, Muhammad G, Masud M (2023) Medical image-based detection of covid-19 using deep convolution neural networks. Multimedia Syst 29(3):1729–1738

    Article  Google Scholar 

  14. Yuan F, Zhang Z, Fang Z (2023) An effective cnn and transformer complementary network for medical image segmentation. Pattern Recogn 136:109228

    Article  Google Scholar 

  15. Ding W, Wang H, Huang J, Hengrong J, Geng Yu, Lin C-T, Pedrycz W (2023) Ftranscnn: Fusing transformer and a cnn based on fuzzy logic for uncertain medical image segmentation. Inf Fusion 99:101880

    Article  Google Scholar 

  16. Xin W, Feng Y, Hong X, Lin Z, Chen T, Li S, Qiu S, Liu Q, Ma Y, Zhang S (2023) Ctranscnn: Combining transformer and cnn in multilabel medical image classification. Knowl-Based Syst 281:111030

    Article  Google Scholar 

  17. Ashwath VA, Sikha OK, Benitez Raul (2023) Ts-cnn: a three-tier self-interpretable cnn for multi-region medical image classification. IEEE access

  18. Krichen M (2023) Convolutional neural networks: A survey. Computers 12(8):151

    Article  Google Scholar 

  19. Derry A, Krzywinski M, Altman N (2023) Convolutional neural networks. Nat Methods 20(9):1269–1270

    Article  Google Scholar 

  20. Atasever S, Azginoglu N, Terzi DS, Terzi R (2023) A comprehensive survey of deep learning research on medical image analysis with focus on transfer learning. Clin Imaging 94:18–41

    Article  Google Scholar 

  21. Öztürk C, Taşyürek M, Türkdamar MU (2023) Transfer learning and fine-tuned transfer learning methods’ effectiveness analyse in the cnn-based deep learning models. Concurr Comput Practice Exp 35(4):e7542

    Article  Google Scholar 

  22. Athar A, Asif RN, Saleem M, Munir S, Nasar MRAl, Momani AM (2023) Improving pneumonia detection in chest x-rays using transfer learning approach (alexnet) and adversarial training. In: 2023 International Conference on Business Analytics for Technology and Security (ICBATS), pp 1–7. IEEE

  23. Kundur NC, Anil BC, Dhulavvagol PM, Ganiger R, Ramadoss B (2023) Pneumonia detection in chest x-rays using transfer learning and tpus. Eng Technol Appl Sci Res 13(5):11878–11883

    Article  Google Scholar 

  24. Schuld M, Sinayskiy I, Petruccione F (2015) An introduction to quantum machine learning. Contemp Phys 56(2):172–185

    Article  Google Scholar 

  25. Biamonte J, Wittek P, Pancotti N, Rebentrost P, Wiebe N, Lloyd S (2017) Quantum machine learning. Nature 549(7671):195–202

    Article  Google Scholar 

  26. Ciliberto C, Herbster M, Ialongo AD, Pontil M, Rocchetto A, Severini S, Wossnig L (2018) Quantum machine learning: a classical perspective. Proc Royal Soc A Math Phys Eng Sci 474(2209):20170551

    MathSciNet  Google Scholar 

  27. Gupta S, Zia RKP (2001) Quantum neural networks. J Comput Syst Sci 63(3):355–383

    Article  MathSciNet  Google Scholar 

  28. Suchara M, Alexeev Y, Chong F, Finkel H, Hoffmann H, Larson J, Osborn J, Smith G (2018) Hybrid quantum-classical computing architectures. In: Proceedings of the 3rd International Workshop on Post-Moore Era Supercomputing, 2018

  29. Gong L-H, Pei J-J, Zhang T-F, Zhou N-R (2024) Quantum convolutional neural network based on variational quantum circuits. Optics Commun 550:129993

    Article  Google Scholar 

  30. Cerezo M, Arrasmith A, Babbush R, Benjamin SC, Endo S, Fujii K, McClean JR, Mitarai K, Yuan X, Cincio L et al (2021) Variational quantum algorithms. Nature Rev Phys 3(9):625–644

    Article  Google Scholar 

  31. Liu J, Lim KH, Wood KL, Huang W, Guo C, Huang H-L (2021) Hybrid quantum-classical convolutional neural networks. Sci China Phys Mech Astron 64(9):290311

    Article  Google Scholar 

  32. Arthur D, Date P (2022) Hybrid quantum-classical neural networks. In: 2022 IEEE International Conference on Quantum Computing and Engineering (QCE), pp. 49–55. IEEE

  33. Fan F, Shi Y, Guggemos T, Zhu Xiao X (2023) Hybrid quantum-classical convolutional neural network model for image classification. IEEE Transactions on Neural Networks and Learning Systems

  34. Li W, Chu P-C, Liu G-Z, Tian Y-B, Qiu T-H, Wang S-M (2022) An image classification algorithm based on hybrid quantum classical convolutional neural network. Quantum Eng 2022(1):5701479

    Google Scholar 

  35. Ren Z, Lan Q, Zhang Y, Wang S (2024) Exploring simple triplet representation learning. Comput Struct Biotechnol J 23:1510–1521

    Article  Google Scholar 

  36. Ren Z, Zhang Y, Wang S (2022) A hybrid framework for lung cancer classification. Electronics 11(10):1614

    Article  Google Scholar 

  37. Cao H, Wang Y, Chen J, Jiang D, Zhang X, Tian Q, Wang M (2022) Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp 205–218. Springer

  38. Perera S, Navard P, Yilmaz A(2024) Segformer3d: an efficient transformer for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4981–4988

  39. Kermany DS, Goldbaum M, Cai W, Valentim CCS, Liang H, Baxter SL, McKeown A, Yang G, Wu X, Yan F et al (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5):1122–1131

    Article  Google Scholar 

  40. Sorensen L, Shaker SB, De Bruijne M (2010) Quantitative analysis of pulmonary emphysema using local binary patterns. IEEE Trans Med Imaging 29(2):559–569

    Article  Google Scholar 

  41. Shaker SB, von Wachenfeldt KA, Larsson S, Mile I, Persdotter S, Dahlbäck M, Broberg P, Stoel B, Bach KS, Hestad M et al (2008) Identification of patients with chronic obstructive pulmonary disease (copd) by measurement of plasma biomarkers. Clin Respir J 2(1):17–25

    Article  Google Scholar 

  42. Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp 448–456. pmlr

  43. Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp 249–256. JMLR Workshop and Conference Proceedings

  44. Ansel J, Yang E, He H, Gimelshein N, Jain A, Voznesensky M, Bao B, Bell P, Berard D, Burovski E, et al. (2024) Pytorch 2: Faster machine learning through dynamic python bytecode transformation and graph compilation. In: Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Vol 2, pp 929–947

  45. Javadi-Abhari A, Treinish M, Krsulich K, Wood CJ, Lishman J, Gacon J, Martiel S, Nation PD., Bishop LS, Cross AW, Johnson BR, Gambetta JM (2024) Quantum computing with Qiskit

Download references

Author information

Authors and Affiliations

  1. Faculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran

    Safura Oviesi & Mohamad Jafar Tarokh

  2. Faculty of Medicine, Zahedan University of Medical Sciences, Zahedan, Iran

    Mohamad kazem Momeni

Authors
  1. Safura Oviesi
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Mohamad Jafar Tarokh
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Mohamad kazem Momeni
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis and implementation were performed by [safura oviesi], [mohamadjafar tarokh] and [mohamadkazem momeni]. The first draft of manuscript was written by [safura oviesi] and all authors commented on previous versions of the manuscript.

Corresponding author

Correspondence to Mohamad Jafar Tarokh.

Ethics declarations

Conflict of interest

The authors declare no Conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Oviesi, S., Tarokh, M.J. & Momeni, M.k. Quantum neural network-assisted learning for small medical datasets: a case study in emphysema detection. J Supercomput 81, 308 (2025). https://doi.org/10.1007/s11227-024-06740-3

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11227-024-06740-3

Keywords