Skip to main content
SpringerLink
Log in

An approach for brain tumour detection based on dual-tree complex Gabor wavelet transform and neural network using Hadoop big data analysis

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Segmentation and classification of the abnormalities on the brain are necessary to save one’s life; hence the data acquired by magnetic resonance imaging (MR imaging) scan have to be processed. Handling massive MR imaging data for high accuracy and precision is a major concern for any framework. Big data and image processing are integrated for brain tumor classification and segmentation in this work. The Hadoop system on MATLAB performs the big data analysis of the brain tumor image. The BraTS dataset is provided to the Hadoop and Matlab distributed computing server (MDCS) system for processing, processed by the single master node and four slave nodes (multimode) on the MDCS configuration. The data from this analysis is decomposed by the novel dual-tree complex Gabor wavelet transform (DTCGWT). The resulting feature vectors are classified as malignant and benign brain tumors based on the deep convolutional neural network (DCNN). If a malignant brain tumor is classified, then the fuzzy level set method based on the manta ray foraging algorithm (FLSM-MRF) will segment the portions of the brain tumor. The model is implemented in the MATLAB platform and has yield minimum of 56.8 min for processing ~30GB of data, while on image processing, 99.1234% and 99.15% accurate result for classification and segmentation respectively is obtained. The parameters like accuracy, sensitivity, specificity, dice, and Jaccard similarity indexes are compared with the existing methods.

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

Similar content being viewed by others

References

  1. Afshar P, Plataniotis KN, Mohammadi A (2019) Capsule networks for brain tumor classification based on MRI images and coarse tumor boundaries. In ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP), 1368-1372

  2. Al-Hadidi MR, AlSaaidah B, Al-Gawagzeh MY (2020) Glioblastomas brain tumour segmentation based on convolutional neural networks. Int J Electrical Comp Eng 10:2088–8708

    Google Scholar 

  3. Amarapur B (2019) Cognition-based MRI brain tumor segmentation technique using modified level set method. Cogn Tech Work 21(3):357–369

    Article  Google Scholar 

  4. Amin J, Sharif M, Yasmin M, Fernandes SL (2017) A distinctive approach in brain tumor detection and classification using MRI. Pattern Recogn Lett

  5. 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

    Article  Google Scholar 

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

    Article  Google Scholar 

  7. Aswathy SU, Devadhas GG, Kumar SS (2020) A tumour segmentation approach from FLAIR MRI brain images using SVM and genetic algorithm. Int J Biomed Eng Technol 33(4):386–397

    Article  Google Scholar 

  8. Baliarsingh SK, Vipsita S, Gandomi AH, Panda A, Bakshi S, Ramasubbareddy S (2020) Analysis of high-dimensional genomic data using MapReduce based probabilistic neural network. Comput Methods Prog Biomed 195:105625

    Article  Google Scholar 

  9. Banerjee S, Bhattacharya M (2010) Segmentation of medical images using selective binary and Gaussian filtering regularized level set (SBGFRLS) method. In 2010 3rd international conference on biomedical engineering and informatics. IEEE 2:541–545

    Google Scholar 

  10. Begum SS, Lakshmi DR (2020) Combining optimal wavelet statistical texture and recurrent neural network for tumour detection and classification over MRI. Multimed Tools Appl 79:1–22

    Article  Google Scholar 

  11. Chahal PK, Pandey S (2020) An efficient Hadoop-based brain tumor detection framework using big data analytic. Software: Practice and Experience

  12. Chen M, Hao Y, Hwang K, Wang L, Wang L (2017) Disease prediction by machine learning over big data from healthcare communities. IEEE Access 5:8869–8879

    Article  Google Scholar 

  13. Dong H, Yang G, Liu F, Mo Y, Guo Y (2017) Automatic brain tumor detection and segmentation using u-net based fully convolutional networks. In annual conference on medical image understanding and analysis, Springer, Cham, 506–517

  14. Elshawi R, Sakr S, Talia D, Trunfio P (2018) Big data systems meet machine learning challenges: towards big data science as a service. Big data research 14:1–11

    Article  Google Scholar 

  15. Emblem KE, Nedregaard B, Hald JK, Nome T, Tonnessen PD, Bjornerud A (2009) Automatic glioma characterization from dynamic susceptibility contrast imaging: brain tumor segmentation using knowledge-based fuzzy clustering. J Magnet Resonance Imag An Official J Int Soc Magnet Resonance Med 30(1):1–10

    Google Scholar 

  16. Forouzanfar M, Forghani N, Teshnehlab M (2010) Parameter optimization of improved fuzzy c-means clustering algorithm for brain MR image segmentation. Eng Appl Artif Intell 23(2):160–168

    Article  Google Scholar 

  17. Giudice PL, Musarella L, Sofo G, Ursino D (2019) An approach to extracting complex knowledge patterns among concepts belonging to structured, semi-structured and unstructured sources in a data lake. Inf Sci 478:606–626

    Article  Google Scholar 

  18. Isunuri BV, Kakarla J (2020) Fast brain tumour segmentation using optimized U-net and adaptive thresholding. Automatika: časopis za automatiku, mjerenje, elektroniku, računarstvo i komunikacije 61(3):352–360

    Article  Google Scholar 

  19. Karameh FN, Dahleh MA (2000) Automated classification of EEG signals in brain tumor diagnostics. In proceedings of the 2000 American control conference. ACC (IEEE cat. No. 00CH36334), 6: 4169-4173

  20. Khan MA, Lali IU, Rehman A, Ishaq M, Sharif M, Saba T, Zahoor S, Akram T (2019) Brain tumor detection and classification: a framework of marker-based watershed algorithm and multilevel priority features selection. Microsc Res Tech 82(6):909–922

    Article  Google Scholar 

  21. Krishnakumar S, Manivannan K (2020) Effective segmentation and classification of brain tumor using rough K means algorithm and multi kernel SVM in MR images. J Ambient Int Human Computing 1-10

  22. Kumar RL, Kakarla J, Isunuri BV, Singh M (2021) Multi-class brain tumor classification using residual network and global average pooling. Multimed Tools Appl 80:1–10

    Article  Google Scholar 

  23. Le THN, Gummadi R, Savvides M (2018) Deep recurrent level set for segmenting brain tumors. In International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, 646–653

  24. Lee I (2017) Big data: dimensions, evolution, impacts, and challenges. Business Horizons 60(3):293–303

    Article  Google Scholar 

  25. Li N, Xiong Z (2019) Automated brain tumor segmentation from multi-modality MRI data based on Tamura texture feature and SVM model. J Physics: Conference Series, IOP Publishing 1168(3):032068

    Google Scholar 

  26. Lok KH, Shi L, Zhu X, Wang D (2017) Fast and robust brain tumor segmentation using level set method with multiple image information. J X-ray Sci Technol 25(2):301–312

    Article  Google Scholar 

  27. Lukas L, Devos A, Suykens JAK, Vanhamme L, Howe FA, Majós C, Moreno-Torres A, van der Graaf M, Tate AR, Arús C, van Huffel S (2004) Brain tumor classification based on long echo proton MRS signals. Artif Intell Med 31(1):73–89

    Article  Google Scholar 

  28. Manogaran G, Vijayakumar V, Varatharajan R, Kumar PM, Sundarasekar R, Hsu C-H (2018) Machine learning based big data processing framework for cancer diagnosis using hidden Markov model and GM clustering. Wirel Pers Commun 102(3):2099–2116

    Article  Google Scholar 

  29. Mohammadpoor M, Torabi F (2018) Big data analytics in oil and gas industry: an emerging trend. Petroleum

  30. Nabizadeh N, Kubat M, John N, Wright C (2013) Efficacy of Gabor-wavelet versus statistical features for brain tumor classification in MRI: a comparative study. In proceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV), the steering Committee of the World Congress in computer science, computer engineering and applied computing (WorldComp), 1

  31. Nazir M, Khan MA, Saba T, Rehman A (2019) Brain tumor detection from MRI images using multi-level wavelets. In: 2019 international conference on computer and information sciences (ICCIS), IEEE, pp 1–5

  32. Özyurt F, Sert E, Avci E, Dogantekin E (2019) Brain tumor detection based on convolutional neural network with neutrosophic expert maximum fuzzy sure entropy. Measurement 147:106830

    Article  Google Scholar 

  33. Park HS, Kim BC, Yeo HY, Kim K-H, Yoo BC, Park JW, Chang HJ (2018) Deleted in malignant brain tumor 1 is a novel prognostic marker in colorectal cancer. Oncol Rep 39(5):2279–2287

    Google Scholar 

  34. Richins G, Stapleton A, Stratopoulos TC, Wong C (2017) Big data analytics: opportunity or threat for the accounting profession? J Inf Syst 31(3):63–79

    Google Scholar 

  35. Saba T, Mohamed AS, El-Affendi M, Amin J, Sharif M (2020) Brain tumor detection using fusion of hand crafted and deep learning features. Cogn Syst Res 59:221–230

    Article  Google Scholar 

  36. Saggi MK, Jain S (2018) A survey towards an integration of big data analytics to big insights for value-creation. Inf Process Manag 54(5):758–790

    Article  Google Scholar 

  37. 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 

  38. Sajjad M, Khan S, Muhammad K, Wu W, Ullah A, Baik SW (2019) Multi-grade brain tumor classification using deep CNN with extensive data augmentation. J Comput Sci 30:174–182

    Article  Google Scholar 

  39. Saxena S, Kumari N, Pattnaik S (2021) Brain tumour segmentation in FLAIR MRI using sliding window texture feature extraction followed by fuzzy C-means clustering. Int J Healthcare Inf Systems Inf (IJHISI) 16(3):1–20

    Article  Google Scholar 

  40. Scala M, Torella A, Severino M, Morana G, Castello R, Accogli A, Verrico A et al (2019) Three de novo DDX3X variants associated with distinctive brain developmental abnormalities and brain tumor in intellectually disabled females. Eur J Hum Genet 27(8):1254–1259

    Article  Google Scholar 

  41. Shakeel PM, El Tobely TE, 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 

  42. Taheri S, Ong SH, Chong VFH (2010) Level-set segmentation of brain tumors using a threshold-based speed function. Image Vis Comput 28(1):26–37

    Article  Google Scholar 

  43. Tandel GS, Balestrieri A, Jujaray T, Khanna NN, Saba L, Suri JS (2020) Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm. Comput Biol Med 122:103804

    Article  Google Scholar 

  44. Thejaswini P, Ms Bhat B, Mr Prakash K (2019) Detection and classification of tumour in brain MRI. Int J Eng Manufact (IJEM) 9(1):11–20

    Article  Google Scholar 

  45. Usman K, Rajpoot K (2017) Brain tumor classification from multi-modality MRI using wavelets and machine learning. Pattern Anal Applic 20(3):871–881

    Article  MathSciNet  Google Scholar 

  46. Wang S, Lu S, Dong Z, Yang J, Yang M, Zhang Y (2016) Dual-tree complex wavelet transform and twin support vector machine for pathological brain detection. Appl Sci 6(6):169

    Article  Google Scholar 

  47. Wu M-N, Lin C-C, Chang C-C (2007) Brain tumor detection using color-based k-means clustering segmentation. In third international conference on intelligent information hiding and multimedia signal processing (IIH-MSP 2007), 2: 245-250

  48. Zhou J, Chan KL, Chong VFH, Krishnan SM (2006) Extraction of brain tumor from MR images using one-class support vector machine. In 2005 IEEE engineering in medicine and biology 27th annual conference, 6411-6414

Download references

Author information

Authors and Affiliations

  1. NTT DATA Canada, Halifax, Canada

    Santhosh Kumar H S

  2. Graphic Era Deemed to be University, Dehradun, Uttarakhand, 248002, India

    K. Karibasappa

Authors
  1. Santhosh Kumar H S
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Karibasappa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Santhosh Kumar H S.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Santhosh Kumar H S, Karibasappa, K. An approach for brain tumour detection based on dual-tree complex Gabor wavelet transform and neural network using Hadoop big data analysis. Multimed Tools Appl 81, 39251–39274 (2022). https://doi.org/10.1007/s11042-022-13016-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-13016-6

Keywords

Search

Navigation