Skip to main content
SpringerLink
Log in

Detection of brain space-occupying lesions using quantum machine learning

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The brain is a complex organ of the body. Any abnormality in brain cells can affect the function of the human body. Brain space-occupying lesions include tumors, abscesses, and cysts. Brain MRI images are noisy that degrades the detection accuracy. Therefore, 32-layers-denoise neural network is proposed on the selected hyper-parameters to improve the image quality. To classify the healthy/abnormal MRI slices, a novel seven layers Javeria Quanvolutional Neural Network model is proposed named as J. Qnet, that consists of the four dense, two drop-out, and one flattened layers. To localize the classified images, the open exchange neural network (ONNX)-YOLOv2tiny model is proposed based on the selected layers that is trained on the optimal hyper-parameters. To segment the localized images more accurately, 34 layers of U-net model are proposed, which is trained from the scratch using selected hyperparameters. The proposed model is evaluated on locally acquired images and BRATS-2020 dataset providing an accuracy of 0.96 and 0.98, respectively. Overall, the proposed method performed better as compared to the existing research works that authenticate the novelty of this work.

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

Access this article

Log in via an institution

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
Fig. 17
Fig. 18

Similar content being viewed by others

Data availability

https://www.med.upenn.edu/cbica/brats2020/data.html.

References

  1. Livneh I, Moshitch-Moshkovitz S, Amariglio N, Rechavi G, Dominissini D (2020) The m6A epitranscriptome: transcriptome plasticity in brain development and function. Nat Rev Neurosci 21(1):36–51

    Google Scholar 

  2. Muhammad N et al (2018) Neurochemical alterations in sudden unexplained perinatal deaths—a review. Front Pediatr 6:6

    MathSciNet  Google Scholar 

  3. Rouhi R, Jafari M, Kasaei S, Keshavarzian P (2015) Benign and malignant breast tumors classification based on region growing and CNN segmentation. Expert Syst Appl 42(3):990–1002

    Google Scholar 

  4. Bello, B, Reichert H, Hirth F (2006) The brain tumor gene negatively regulates neural progenitor cell proliferation in the larval central brain of Drosophila

  5. Amin J, Sharif M, Yasmin M, Saba T, Raza M (2020) Use of machine intelligence to conduct analysis of human brain data for detection of abnormalities in its cognitive functions. Multimed Tools Appl 79:10955–10973

    Google Scholar 

  6. Kransdorf MJ, Murphey MD (2000) Radiologic evaluation of soft-tissue masses: a current perspective. Am J Roentgenol 175(3):575–587

    Google Scholar 

  7. Sharif MI, Li JP, Amin J, Sharif A (2021) An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network. Complex Intell Syst 7:2023–2036

    Google Scholar 

  8. Sirko A, Dzyak L, Chekha E (2020) Coexistence of multiple sclerosis and brain tumors: a literature review. Meдичнi пepcпeктиви 25(2):30–36

    Google Scholar 

  9. Bailey DL et al (2013) Summary report of the first international workshop on PET/MR Imaging, March 19–23, 2012, Tübingen, Germany. Mol Imag Biol 15(4):361–371

    Google Scholar 

  10. Handelman G, Kok H, Chandra R, Razavi A, Lee M, Asadi H (2018) eD octor: machine learning and the future of medicine. J Intern Med 284(6):603–619

    Google Scholar 

  11. Zhang Y, Yang J, Wang S, Dong Z, Phillips P (2017) Pathological brain detection in MRI scanning via Hu moment invariants and machine learning. J Exp Theor Artif Intell 29(2):299–312

    Google Scholar 

  12. Soltaninejad M et al (2018) Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels. Comput Methods Programs Biomed 157:69–84

    Google Scholar 

  13. Han C, et al (2020) Infinite brain MR images: PGGAN-based data augmentation for tumor detection. In: Neural approaches to dynamics of signal exchanges: Springer, 2020, pp. 291–303

  14. Chen X, You S, Tezcan KC, Konukoglu E (2020) Unsupervised lesion detection via image restoration with a normative prior. Med Image Anal 64:101713

    Google Scholar 

  15. Liu Z, et al (2020) Deep learning based brain tumor segmentation: a survey. arXiv preprint arXiv:2007.09479

  16. Amin J, Sharif M, Haldorai A, Yasmin M, Nayak RS (2021) Brain tumor detection and classification using machine learning: a comprehensive survey. Complex Intell Syst 8:3161–3183

    Google Scholar 

  17. Akbar AS, Fatichah C, Suciati N (2022) Single level UNet3D with multipath residual attention block for brain tumor segmentation. J King Saud Univ Comput Inf Sci 34(6):3247–3258

    Google Scholar 

  18. Allah AMG, Sarhan AM, Elshennawy NM (2023) Edge U-Net: brain tumor segmentation using MRI based on deep U-Net model with boundary information. Expert Syst Appl 213:118833

    Google Scholar 

  19. Fang L, Wang X (2023) Multi-input Unet model based on the integrated block and the aggregation connection for MRI brain tumor segmentation. Biomed Signal Process Control 79:104027

    Google Scholar 

  20. Raza R, Bajwa UI, Mehmood Y, Anwar MW, Jamal MH (2023) dResU-Net: 3D deep residual U-Net based brain tumor segmentation from multimodal MRI. Biomed Signal Process Control 79:103861

    Google Scholar 

  21. Litjens G et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88

    Google Scholar 

  22. Zhang J, Lv X, Zhang H, Liu B (2020) AResU-Net: attention residual U-Net for brain tumor segmentation. Symmetry 12(5):721

    Google Scholar 

  23. Nazir M, Wahid F, Ali Khan S (2015) A simple and intelligent approach for brain MRI classification. J Intell Fuzzy Syst 28(3):1127–1135

    MathSciNet  Google Scholar 

  24. Toğaçar M, Cömert Z, Ergen B (2020) Classification of brain MRI using hyper column technique with convolutional neural network and feature selection method. Expert Syst Appl 149:113274

    Google Scholar 

  25. Amin J, Sharif M, Anjum MA, Raza M, Bukhari SAC (2020) Convolutional neural network with batch normalization for glioma and stroke lesion detection using MRI. Cogn Syst Res 59:304–311

    Google Scholar 

  26. Muhammad K, Khan S, Del Ser J, De Albuquerque VHC (2020) Deep learning for multigrade brain tumor classification in smart healthcare systems: a prospective survey. IEEE Trans Neural Netw Learn Syst 32(2):507–522

    Google Scholar 

  27. Amin J, Sharif M, Yasmin M, Fernandes SL (2020) A distinctive approach in brain tumor detection and classification using MRI. Pattern Recogn Lett 139:118–127

    Google Scholar 

  28. Amin J, Anjum MA, Sharif M, Jabeen S, Kadry S, Moreno Ger P (2022) A new model for brain tumor detection using ensemble transfer learning and quantum variational classifier. Comput Intell Neurosci. 2022

  29. Amin J et al (2020) Brain tumor detection by using stacked autoencoders in deep learning. J Med Syst 44:1–12

    Google Scholar 

  30. Amin J, Sharif M, Raza M, Saba T, Rehman A (2019) Brain tumor classification: feature fusion. In: 2019 international conference on computer and information sciences (ICCIS), pp. 1–6: IEEE

  31. Mzoughi H, Njeh I, Slima MB, Ben Hamida A, Mhiri C, Mahfoudh KB (2021) Towards a computer aided diagnosis (CAD) for brain MRI glioblastomas tumor exploration based on a deep convolutional neuronal networks (D-CNN) architectures. Multimed Tools Appl 80(1):899–919

    Google Scholar 

  32. Amin J, Sharif M, Yasmin M, Fernandes SL (2018) Big data analysis for brain tumor detection: deep convolutional neural networks. Futur Gener Comput Syst 87:290–297

    Google Scholar 

  33. Sharif MI, Li JP, Khan MA, Saleem MA (2020) Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images. Pattern Recogn Lett 129:181–189

    Google Scholar 

  34. Sharif M, Amin J, Raza M, Yasmin M, Satapathy SC (2020) An integrated design of particle swarm optimization (PSO) with fusion of features for detection of brain tumor. Pattern Recogn Lett 129:150–157

    Google Scholar 

  35. Amin J, Sharif M, Gul N, Kadry S, Chakraborty C (2021) Quantum machine learning architecture for COVID-19 classification based on synthetic data generation using conditional adversarial neural network. Cognit Comput 14(5):1677–1688

    Google Scholar 

  36. Amin J, Sharif M, Raza M, Saba T, Sial R, Shad SA (2020) Brain tumor detection: a long short-term memory (LSTM)-based learning model. Neural Comput Appl 32(20):15965–15973

    Google Scholar 

  37. Sharif M, Amin J, Raza M, Anjum MA, Afzal H, Shad SA (2020) Brain tumor detection based on extreme learning. Neural Comput Appl 32(20):15975–15987

    Google Scholar 

  38. Amin J, Sharif M, Yasmin M, Saba T, Anjum MA, Fernandes SL (2019) A new approach for brain tumor segmentation and classification based on score level fusion using transfer learning. J Med Syst 43(11):1–16

    Google Scholar 

  39. Khan MA et al (2020) Multimodal brain tumor classification using deep learning and robust feature selection: A machine learning application for radiologists. Diagnostics 10(8):565

    Google Scholar 

  40. Kiani BT, Villanyi A, Lloyd S (2020) Quantum medical imaging algorithms. arXiv preprint arXiv:2004.02036

  41. Konar D, Bhattacharyya S, Gandhi TK, Panigrahi BK (2020) A quantum-inspired self-supervised network model for automatic segmentation of brain MR images. Appl Soft Comput 93:106348

    Google Scholar 

  42. Konar D, Bhattacharyya S, Panigrahi BK, Behrman EC (2021) Qutrit-inspired fully self-supervised shallow quantum learning network for brain tumor segmentation. IEEE Trans Neural Netw Learn Syst 33(11):6331–6345

    MathSciNet  Google Scholar 

  43. Zhang K, Zuo W, Chen Y, Meng D, Zhang L (2017) Beyond a Gaussian denoiser: residual learning of deep cnn for image denoising. IEEE Trans Image Process 26(7):3142–3155

    MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

  45. Redmon F,Redmon J, Farhadi A (2017) Yolo9000: Better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), IEEE Computer Society, pp. 6517–6525

  46. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779–788

  47. Open Neural Network Exchange. https://github.com/onnx/, accessed by 12/3/2023

  48. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18, pp. 234–241: Springer

  49. Eltayeb EN, Salem NM, Al-Atabany W (2019) Automated brain tumor segmentation from multi-slices FLAIR MRI images. Bio-Med Mater Eng 30(4):449–462

    Google Scholar 

  50. Rehman ZU, Zia MS, Bojja GR, Yaqub M, Jinchao F, Arshid K (2020) Texture based localization of a brain tumor from MR-images by using a machine learning approach. Med Hypotheses 141:109705

    Google Scholar 

  51. Babushkina EA, Belokopytova LV, Grachev AM, Meko DM, Vaganov EA (2017) Variation of the hydrological regime of Bele-Shira closed basin in Southern Siberia and its reflection in the radial growth of Larix sibirica. Reg Environ Change 17:1725–1737

    Google Scholar 

  52. Chithra P, Dheepa G (2020) Di-phase midway convolution and deconvolution network for brain tumor segmentation in MRI images. Int J Imaging Syst Technol 30(3):674–686

    Google Scholar 

  53. Rao CS, Karunakara K (2022) Efficient detection and classification of brain tumor using Kernel based SVM for MRI. Multimed Tools Appl 81(5):7393–7417

    Google Scholar 

  54. Gull S, Akbar S, Hassan SA, Rehman A, Sadad T (2022) Automated brain tumor segmentation and classification through MRI images. In: International Conference on Emerging Technology Trends in Internet of Things and Computing, pp. 182–194: Springer

  55. Montaha S, Azam S, Rafid ARH, Hasan MZ, Karim A, Islam A (2022) Timedistributed-cnn-lstm: a hybrid approach combining cnn and lstm to classify brain tumor on 3D mri scans performing ablation study. IEEE Access 10:60039–60059

    Google Scholar 

  56. Asthana P, Hanmandlu M, Vashisth S (2022) Brain tumor detection and patient survival prediction using U-Net and regression model. Int J Imag Syst Technol 32(5):1801–1814

    Google Scholar 

  57. Mohapatra SK et al (2022) Segmentation and classification of encephalon tumor by applying improved fast and robust FCM algorithm with PSO-based Elm Technique. Comput Intell Neurosci. https://doi.org/10.1155/2022/2664901

    Article  Google Scholar 

  58. Shahin AI, Aly W, Aly S (2023) MBTFCN: a novel modular fully convolutional network for MRI brain tumor multi-classification. Expert Syst Appl 212:118776

    Google Scholar 

  59. Ilhan A, Sekeroglu B, Abiyev R (2022) Brain tumor segmentation in MRI images using nonparametric localization and enhancement methods with U-net. Int J Comput Assist Radiol Surg 17(3):589–600

    Google Scholar 

  60. Tampu IE, Haj-Hosseini N, Eklund A (2020) Does contextual information improve 3D U-Net based brain tumor segmentation?. arXiv preprint arXiv:2010.13460

  61. Nguyen HT, Le TT, Nguyen TV, Nguyen NT (2020) Enhancing MRI brain tumor segmentation with an additional classification network. arXiv preprint arXiv:2009.12111

  62. Henry T, et al (2020) Top 10 BraTS 2020 challenge solution: brain tumor segmentation with self-ensembled, deeply-supervised 3D-Unet like neural networks. arXiv preprint arXiv:2011.01045

  63. Messaoudi H et al (2020) Efficient embedding network for 3D brain tumor segmentation. arXiv preprint arXiv:2011.11052

  64. Sasank V, Venkateswarlu S (2022) An automatic tumour growth prediction based segmentation using full resolution convolutional network for brain tumour. Biomed Signal Process Control 71:103090

    Google Scholar 

  65. T. Henry et al., "Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution," in International MICCAI Brainlesion Workshop, 2021, pp. 327–339: Springer.

  66. Karri M, Annvarapu CSR, Acharya UR (2022) SGC-ARANet: scale-wise global contextual axile reverse attention network for automatic brain tumor segmentation. Appl Intell 53:15407–15423

    Google Scholar 

  67. Ruba T, Tamilselvi R, Beham MP (2023) Brain tumor segmentation using JGate-AttResUNet–A novel deep learning approach. Biomed Signal Process Control 84:104926

    Google Scholar 

  68. Ullah F, Salam A, Abrar M, Amin F (2023) Brain tumor segmentation using a patch-based convolutional neural network: a big data analysis approach. Mathematics 11(7):1635

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Wah, Wah Cantt, Pakistan

    Javaria Amin

  2. National University of Technology (NUTECH), Islamabad, Pakistan

    Muhammad Almas Anjum

  3. FCPS Diagnostic Radiology, Consultant Radiologist POF Hospital and Associate Professor Radiology Wah Medical College, Wah Cantt, Pakistan

    Nadia Gul

  4. COMSATS University Islamabad, Wah Campus, Pakistan

    Muhammad Sharif

Authors
  1. Javaria Amin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muhammad Almas Anjum
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nadia Gul
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Muhammad Sharif
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Javaria Amin.

Ethics declarations

Conflict of interest

All authors declare that there is no conflict of interest in this research.

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

Amin, J., Anjum, M.A., Gul, N. et al. Detection of brain space-occupying lesions using quantum machine learning. Neural Comput & Applic 35, 19279–19295 (2023). https://doi.org/10.1007/s00521-023-08717-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-08717-4

Keywords

Search

Navigation