Skip to main content
SpringerLink
Log in

IFAS: improved fully automatic segmentation convolutional neural network model along with morphological segmentation for brain tumor detection

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

Image segmentation of brain Magnetic Resonance Imaging (MRI) images is one of the trusted method for tumor detection. The tumors are dynamically grown and can be present in any size and shape. Segmenting the tumor region from tumor affected images with high accuracy is a great challenge. This paper addresses the problem and proposes improved fully-automatic segmentation (IFAS) Convolutional Neural Network model. In IFAS, morphological operation along with a fully- automatic segmentation algorithm on brain MRI images is applied. For the analysis of the fully-automatic segmentation method, the U-net structure is considered for morphological segmentation and the CNN model is used for classification. Three online brain MRI datasets are used for the evaluation and testing of the designed model for brain MRI image classification and segmentation. The proposed model is trained for all three datasets in two stages and testing is conducted for the other two datasets. In the first stage, the model is trained with original MRI images and tested for the other two datasets, with an average dice similarity of 0.707 for dataset 1, 0.7575 for dataset 2, and 0.6063 for dataset 3 being observed respectively. In the second stage, the model is trained on morphologically enhanced images, and then testing is done with the original images of the other two datasets. The average dice similarity observed in the latter case is 0.888 for morphological enhanced dataset 1, 0.791 for morphological enhanced dataset 2, and 0.7197 for morphological enhanced dataset 3. The observed results show that the IFAS model trained on morphologically enhanced images performs better with 99.36% of training accuracy and 98.84% validation accuracy is achieved.

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

Similar content being viewed by others

Availability of data and materials

The data may be available on demand.

References

  1. Dolz J, Gopinath K, Yuan J, Lombaert H, Desrosiers C, Ayed IB (2018) HyperDense-Net: a hyper-densely connected CNN for multi-modal image segmentation. IEEE Trans Med Imaging 38(5):1116–1126

    Article  PubMed  Google Scholar 

  2. Bao S, Chung AC (2018) Multi-scale structured CNN with label consistency for brain MR image segmentation. Comput Methods Biomech Biomed Eng Imaging & Vis 6(1):113–117

    Article  Google Scholar 

  3. 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

    Article  ADS  Google Scholar 

  4. Talo M, Yildirim O, Baloglu UB, Aydin G, Acharya UR (2019) Convolutional neural networks for multi-class brain disease detection using MRI images. Comput Med Imaging Graph 78:101673

    Article  PubMed  Google Scholar 

  5. Çinar A, Yildirim M (2020) Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture. Med Hypotheses 139:109684

    Article  PubMed  Google Scholar 

  6. Zhang YD, Govindaraj VV, Tang C, Zhu W, Sun J (2019) High-performance multiple sclerosis classification by data augmentation and AlexNet transfer learning model. J Med Imaging Health Inform 9(9):2012–2021

    Article  Google Scholar 

  7. Khairandish MO, Sharma M, Jain V, Chatterjee JM, Jhanjhi NZ (2021) A hybrid CNN-SVM threshold segmentation approach for tumor detection and classification of MRI brain images. IRBM 43(4):290–299

    Article  Google Scholar 

  8. Amin J, Sharif M, Raza M, Saba T, Anjum MA (2019) Brain tumor detection using statistical and machine learning method. Comput Methods Programs Biomed 177:69–79

    Article  PubMed  Google Scholar 

  9. Shivaprasad BJ (2021) Bidirectional ConvLSTMXNet for brain tumor segmentation of MR images. Tehnički glasnik 15(1):37–42

    Article  Google Scholar 

  10. Iqbal S, Ghani MU, Saba T, Rehman A (2018) Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN). Microsc Res Tech 81(4):419–427

    Article  PubMed  Google Scholar 

  11. Shakeel PM, Tobely TEF, Al-Feel H, Manogaran G, Baskar S (2019) Neural network-based brain tumor detection using wireless infrared imaging sensor. IEEE Access 7:5577–5588

    Article  Google Scholar 

  12. Mittal M, Goyal LM, Kaur S, Kaur I, Verma A et al (2019) Deep learning based enhanced tumor segmentation approach for MR brain images. Appl Soft Comput 78:346–354

    Article  Google Scholar 

  13. Xue Y, Xu T, Zhang H, Long LR, Huang X (2018) SegAN: adversarial network with multi-scale L1 loss for medical image segmentation. Neuroinformatics 16(3):383–392

    Article  PubMed  Google Scholar 

  14. Zhang W, Li R, Deng H, Wang L, Lin W, Ji S, Shen D (2015) Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. Neuroimage 108:214–224

    Article  PubMed  Google Scholar 

  15. Bernal J, Kushibar K, Asfaw DS, Valverde S, Oliver A, Martí R, Lladó X (2019) Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review. Artif Intell Med 95:64–81

    Article  PubMed  Google Scholar 

  16. Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin P-M, Larochelle H (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18–31

    Article  PubMed  Google Scholar 

  17. Chang J, Zhang L, Gu N, Zhang X, Ye M, Yin R, Meng Q (2019) A mix-pooling CNN architecture with FCRF for brain tumor segmentation. J Vis Commun Image Represent 58:316–322

    Article  Google Scholar 

  18. Pereira S, Pinto A, Alves V, Silva CA (2016) Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging 35(5):1240–1251

    Article  PubMed  Google Scholar 

  19. Wang G, Li W, Aertsen M, Deprest J, Ourselin S et al (2019) Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks. Neurocomputing 338:34–45

    Article  Google Scholar 

  20. Moeskops P, Jelmer M, Wolterink B, Viergever MA, Išgum I et al (2016) Deep learning for multi-task medical image segmentation in multiple modalities. In: International Conference on medical image computing and computer-assisted intervention, Springer, Cham, Proceeding part II 19, pp. 478–486

  21. Available: https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection. Accessed 15 June 2022

  22. Available: https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri. Accessed 15 June 2022

  23. Available: https://www.kaggle.com/datasets/abhranta/brain-tumor-detection-mri. Accessed 15 June 2022

  24. Kulshreshtha A, Nagpal A (2021) Analysis of morphological operations on image segmentation techniques. ICTACT J Image Video Process 12(1):2555–2558

    Google Scholar 

  25. Sajid S, Hussain S, Sarwar A (2019) Brain tumor detection and segmentation in MR images using deep learning. Arab J Sci Eng 44(11):9249–9261

    Article  Google Scholar 

  26. Grøvik E, Yi D, Iv M, Tong E, Rubin D, Zaharchuk G (2020) Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI. J Magn Reson Imaging 51(1):175–182

    Article  PubMed  Google Scholar 

  27. Anithadevi D, Perumal K (2016) A hybrid approach based segmentation technique for brain tumor in MRI Images. arXiv preprint arXiv: 1603.02447

  28. Salehi S, Mohseni SS, Erdogmus D, Gholipour A (2017) Tversky loss function for image segmentation using 3D fully convolutional deep networks. In: International Workshop on machine learning in medical imaging, Springer, Cham, Proceedings 8, pp 379–387

  29. Hashemi SR, Sadegh Mohseni Salehi S, Erdogmus D, Prabhu SP et al (2018) Asymmetric loss functions and deep densely-connected networks for highly-imbalanced medical image segmentation: application to multiple sclerosis lesion detection. IEEE Access 7:1721–1735

    Article  Google Scholar 

  30. Srinivas B, Sasibhushana Rao G (2020) Segmentation of multi-modal MRI brain tumor sub-regions using deep learning. J Electric Eng Technol 15(4):1899–1909

    Article  Google Scholar 

  31. Kulshreshtha A, Nagpal A (2023) Brain image segmentation using variation in structural elements of morphological operators. Int J Inform Technol 15:2283–2291

  32. Kaur P (2017) Intuitionistic fuzzy sets based credibilistic fuzzy C-means clustering for medical image segmentation. Int J Inf Technol 9(4):345–351

    Google Scholar 

  33. Rani VJ, Thanammal KK (2023) Lung cancer segmentation using MIBFS clustering and energetic BPN. Int J Inform Technol 15(2):905–916

    Google Scholar 

  34. Silvoster ML, Mathusoothana R, Kumar S (2022) Watershed based algorithms for the segmentation of spine MRI. Int J Inform Technol 14:1343–1353

  35. Kaur R, Ranade SK (2023) Improving accuracy of convolutional neural network-based skin lesion segmentation using group normalization and combined loss function. Int J Inform Technol 15:2827–2835

    Google Scholar 

Download references

Acknowledgements

This work would not have been possible without the support of Abhranta Panigrahi, Navoneel chakrabarty and Masoud Nickparvar as we have used the online dataset of brain MRIimages for study and analysis donated by the them on Kaggle.com.

Author information

Authors and Affiliations

  1. G. D. Goenka University, Sohna, Gurgaon, India

    Akanksha Kulshreshtha & Arpita Nagpal

  2. DPG Institute of Technology & Management, Gurgaon, India

    Akanksha Kulshreshtha

  3. Bharati Vidyapeeth’s Institute of Computer Applications and Management (BVICAM), New Delhi, India

    Arpita Nagpal

Authors
  1. Akanksha Kulshreshtha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arpita Nagpal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Akanksha Kulshreshtha.

Ethics declarations

Conflict of interest

All the authors confirm that there are no conflicts of interest in this study.

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

Kulshreshtha, A., Nagpal, A. IFAS: improved fully automatic segmentation convolutional neural network model along with morphological segmentation for brain tumor detection. Int. j. inf. tecnol. 16, 1517–1525 (2024). https://doi.org/10.1007/s41870-023-01572-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41870-023-01572-5

Keywords

Search

Navigation