Skip to main content
SpringerLink
Log in

YoDenBi-NET: YOLO + DenseNet + Bi-LSTM-based hybrid deep learning model for brain tumor classification

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

Abstract

Brain tumor, which is the deadliest disease in adults, grows rapidly and disrupts the functioning of organs. Brain tumors can be of different types, depending on their shape, texture, and location. The correct detection of these types helps the field specialist to make the correct diagnosis and thus save the patient's life. In this study, a three-stage hybrid new classification framework based on YOLO + DenseNet + Bi-LSTM is proposed to classify glioma, meningioma, and pituitary brain tumor types. In this framework, the brain region is detected first through the YOLO detection algorithm. In the second stage, deep features are extracted from this region via a pre-trained deep learning architecture, and in the final stage, brain tumor classification is performed by way of the Bi-LSTM network which is another deep learning model. The proposed model offers high test accuracies of 99.77% and 99.67%, respectively, for three brain tumor types using hold-out and tenfold cross-validation techniques on a dataset containing 3064 MRI images. With its high performance in validation and test sets, the proposed hybrid model is better than other previous studies and so it can be used as a useful decision support system for field specialists.

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

Availability of data and materials

This study uses public dataset available at: https://figshare.com/articles/dataset/brain_tumor_dataset/1512427/5

References

  1. Abdelaziz Ismael SA, Mohammed A, Hefny H (2020) An enhanced deep learning approach for brain cancer MRI images classification using residual networks. Artif Intell Med 102:101779. https://doi.org/10.1016/J.ARTMED.2019.101779

    Article  Google Scholar 

  2. Kesav N, Jibukumar MG (2021) Efficient and low complex architecture for detection and classification of brain tumor using RCNN with two channel CNN. J King Saud Univ Comput Inf Sci 34(8):6229–6242. https://doi.org/10.1016/J.JKSUCI.2021.05.008

    Article  Google Scholar 

  3. Biju KS, Hakkim HA, Jibukumar MG (2017) Ictal EEG classification based on amplitude and frequency contours of IMFs. Biocybern Biomed Eng 37:172–183. https://doi.org/10.1016/J.BBE.2016.12.005

    Article  Google Scholar 

  4. Shafi ASM, Rahman MB, Anwar T et al (2021) Classification of brain tumors and auto-immune disease using ensemble learning. Inform Med Unlocked 24:100608. https://doi.org/10.1016/J.IMU.2021.100608

    Article  Google Scholar 

  5. WHO (2022) Cancer. https://www.who.int/health-topics/cancer#tab=tab_1. Accessed 23 Feb 2022

  6. Khairandish MO, Sharma M, Jain V et al (2021) A hybrid CNN-SVM threshold segmentation approach for tumor detection and classification of MRI brain images. IRBM 43(4):290–299. https://doi.org/10.1016/J.IRBM.2021.06.003

    Article  Google Scholar 

  7. Öksüz C, Urhan O, Güllü MK (2022) Brain tumor classification using the fused features extracted from expanded tumor region. Biomed Signal Process Control 72(Part B):103356. https://doi.org/10.1016/J.BSPC.2021.103356

    Article  Google Scholar 

  8. Sajjad M, Khan S, Muhammad K et al (2019) Multi-grade brain tumor classification using deep CNN with extensive data augmentation. J Comput Sci 30:174–182. https://doi.org/10.1016/J.JOCS.2018.12.003

    Article  Google Scholar 

  9. Pereira S, Pinto A, Alves V, Silva CA (2016) Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging 35:1240–1251. https://doi.org/10.1109/TMI.2016.2538465

    Article  Google Scholar 

  10. Ahuja S, Panigrahi BK, Gandhi TK (2022) Enhanced performance of Dark-Nets for brain tumor classification and segmentation using colormap-based superpixel techniques. Mach Learn Appl 7:100212. https://doi.org/10.1016/J.MLWA.2021.100212

    Article  Google Scholar 

  11. Pereira S, Meier R, Alves V, et al (2018) Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11038 LNCS, pp 106–114. https://doi.org/10.1007/978-3-030-02628-8_12

  12. Tandel GS, Tiwari A, Kakde OG (2021) Performance optimisation of deep learning models using majority voting algorithm for brain tumour classification. Comput Biol Med 135:104564. https://doi.org/10.1016/J.COMPBIOMED.2021.104564

    Article  Google Scholar 

  13. Komaki K, Sano N, Tangoku A (2006) Problems in histological grading of malignancy and its clinical significance in patients with operable breast cancer. Breast Cancer 13(3):249–253. https://doi.org/10.2325/JBCS.13.249

    Article  Google Scholar 

  14. Kaplan K, Kaya Y, Kuncan M, Ertunç HM (2020) Brain tumor classification using modified local binary patterns (LBP) feature extraction methods. Med Hypotheses 139:109696. https://doi.org/10.1016/J.MEHY.2020.109696

    Article  Google Scholar 

  15. Tiwari A, Srivastava S, Pant M (2020) Brain tumor segmentation and classification from magnetic resonance images: review of selected methods from 2014 to 2019. Pattern Recognit Lett 131:244–260. https://doi.org/10.1016/J.PATREC.2019.11.020

    Article  Google Scholar 

  16. Tandel GS, Biswas M, Kakde OG et al (2019) A review on a deep learning perspective in brain cancer classification. Cancers (Basel) 11(1):111. https://doi.org/10.3390/cancers11010111

    Article  Google Scholar 

  17. Dora L, Agrawal S, Panda R, Abraham A (2018) Nested cross-validation based adaptive sparse representation algorithm and its application to pathological brain classification. Expert Syst Appl 114:313–321. https://doi.org/10.1016/J.ESWA.2018.07.039

    Article  Google Scholar 

  18. Kumar S, Mankame DP (2020) Optimization driven deep convolution neural network for brain tumor classification. Biocybern Biomed Eng 40:1190–1204. https://doi.org/10.1016/J.BBE.2020.05.009

    Article  Google Scholar 

  19. Rehman A, Naz S, Razzak MI et al (2020) A deep learning-based framework for automatic brain tumors classification using transfer learning. Circuits Syst Signal Process 39:757–775. https://doi.org/10.1007/S00034-019-01246-3/TABLES/8

    Article  Google Scholar 

  20. Noreen N, Palaniappan S, Qayyum A et al (2020) A deep learning model based on concatenation approach for the diagnosis of brain tumor. IEEE Access 8:55135–55144. https://doi.org/10.1109/ACCESS.2020.2978629

    Article  Google Scholar 

  21. Huang Z, Du X, Chen L et al (2020) Convolutional neural network based on complex networks for brain tumor image classification with a modified activation function. IEEE Access 8:89281–89290. https://doi.org/10.1109/ACCESS.2020.2993618

    Article  Google Scholar 

  22. Cheng J, Huang W, Cao S et al (2015) Enhanced performance of brain tumor classification via tumor region augmentation and partition. PLoS One 10(12):e0140381. https://doi.org/10.1371/journal.pone.0140381

    Article  Google Scholar 

  23. Gumaei A, Hassan MM, Hassan MR et al (2019) A hybrid feature extraction method with regularized extreme learning machine for brain tumor classification. IEEE Access 7:36266–36273. https://doi.org/10.1109/ACCESS.2019.2904145

    Article  Google Scholar 

  24. Sachdeva J, Kumar V, Gupta I et al (2013) Segmentation, feature extraction, and multiclass brain tumor classification. J Digit Imaging 26:1141–1150. https://doi.org/10.1007/S10278-013-9600-0/TABLES/4

    Article  Google Scholar 

  25. Yin B, Wang C, Abza F (2020) New brain tumor classification method based on an improved version of whale optimization algorithm. Biomed Signal Process Control 56:101728. https://doi.org/10.1016/J.BSPC.2019.101728

    Article  Google Scholar 

  26. Pashaei A, Sajedi H, Jazayeri N (2018) Brain tumor classification via convolutional neural network and extreme learning machines. In: 2018 8th International conference on computer and knowledge engineering, ICCKE 2018. pp 314–319. https://doi.org/10.1109/ICCKE.2018.8566571

  27. Arı A, Hanbay D (2018) Deep learning based brain tumor classification and detection system. Turk J Electr Eng Comput Sci 26:2275–2286. https://doi.org/10.3906/elk-1801-8

    Article  Google Scholar 

  28. Özyurt F, Sert E, Avcı D (2020) An expert system for brain tumor detection: fuzzy C-means with super resolution and convolutional neural network with extreme learning machine. Med Hypotheses 134:109433. https://doi.org/10.1016/J.MEHY.2019.109433

    Article  Google Scholar 

  29. Byale H, Lingaraju GM, Sivasubramanian S (2018) Automatic segmentation and classification of brain tumor using machine learning techniques. Int J Appl Eng Res (IJAER) 13:11686–11692

    Google Scholar 

  30. Hsieh KLC, Lo CM, Hsiao CJ (2017) Computer-aided grading of gliomas based on local and global MRI features. Comput Methods Programs Biomed 139:31–38. https://doi.org/10.1016/J.CMPB.2016.10.021

    Article  Google Scholar 

  31. Zia R, Akhtar P, Aziz A (2018) A new rectangular window based image cropping method for generalization of brain neoplasm classification systems. Int J Imaging Syst Technol 28:153–162. https://doi.org/10.1002/IMA.22266

    Article  Google Scholar 

  32. Anaraki AK, Ayati M, Kazemi F (2019) Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms. Biocybern Biomed Eng 39:63–74. https://doi.org/10.1016/J.BBE.2018.10.004

    Article  Google Scholar 

  33. Choudhury CL, Mahanty C, Kumar R, Mishra BK (2020) Brain tumor detection and classification using convolutional neural network and deep neural network. In: 2020 International conference on computer science, engineering and applications, ICCSEA 2020, pp 1–4. https://doi.org/10.1109/ICCSEA49143.2020.9132874

  34. Ismael MR, Abdel-Qader I (2018) Brain tumor classification via statistical features and back-propagation neural network. In: IEEE International conference on electro ınformation technology 2018-May, pp 252–257. https://doi.org/10.1109/EIT.2018.8500308

  35. Abiwinanda N, Hanif M, Hesaputra ST, et al (2019) Brain tumor classification using convolutional neural network. In: World congress on medical physics and biomedical engineering 2018. IFMBE proceedings, vol 68/1. Springer, Singapore, pp 183–189. https://doi.org/10.1007/978-981-10-9035-6_33

  36. Sultan HH, Salem NM, Al-Atabany W (2019) Multi-classification of brain tumor images using deep neural network. IEEE Access 7:69215–69225. https://doi.org/10.1109/ACCESS.2019.2919122

    Article  Google Scholar 

  37. Kumar A, Ansari MA, Ashok A (2019) A hybrid framework for brain tumor classification using grey wolf optimization and multi-class support vector machine. Int J Recent Technol Eng 8:7746–7752. https://doi.org/10.35940/ijrte.C6315.098319

    Article  Google Scholar 

  38. Paul JS, Plassard AJ, Landman BA, Fabbri D (2017) Deep learning for brain tumor classification. vol 10137, pp 253–268. https://doi.org/10.1117/12.2254195

  39. Das S, Aranya OFMRR, Labiba NN (2019) Brain tumor classification using convolutional neural network. In: 1st International conference on advances in science, engineering and robotics technology, vol 2019, pp 1–12. https://doi.org/10.1109/ICASERT.2019.8934603

  40. Badža MM, Barjaktarović MČ (2020) Classification of brain tumors from MRI images using a convolutional neural network. Appl Sci 10(6):1999. https://doi.org/10.3390/app10061999

    Article  Google Scholar 

  41. Mondal A, Shrivastava VK (2022) A novel Parametric Flatten-p Mish activation function based deep CNN model for brain tumor classification. Comput Biol Med 150:106183. https://doi.org/10.1016/J.COMPBIOMED.2022.106183

    Article  Google Scholar 

  42. Shanthi S, Saradha S, Smitha JA et al (2022) An efficient automatic brain tumor classification using optimized hybrid deep neural network. Int J Intell Netw 3:188–196. https://doi.org/10.1016/J.IJIN.2022.11.003

    Article  Google Scholar 

  43. Aamir M, Rahman Z, Dayo ZA et al (2022) A deep learning approach for brain tumor classification using MRI images. Comput Electr Eng 101:108105. https://doi.org/10.1016/J.COMPELECENG.2022.108105

    Article  Google Scholar 

  44. Aurna NF, Yousuf MA, Taher KA et al (2022) A classification of MRI brain tumor based on two stage feature level ensemble of deep CNN models. Comput Biol Med 146:105539. https://doi.org/10.1016/J.COMPBIOMED.2022.105539

    Article  Google Scholar 

  45. Vankdothu R, Hameed MA, Fatima H (2022) A brain tumor ıdentification and classification using deep learning based on CNN-LSTM method. Comput Electr Eng 101:107960. https://doi.org/10.1016/J.COMPELECENG.2022.107960

    Article  Google Scholar 

  46. Cheng J (2017) Brain tumor dataset. figshare. Dataset. In: Figshare. https://figshare.com/articles/dataset/brain_tumor_dataset/1512427/5. Accessed 10 Nov 2021

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

  48. Wu S, Yang J, Yu H, et al (2021) Gaussian guided IoU: a better metric for balanced learning on object detection. ArXiv 2103.13613

  49. Redmon J, Farhadi A (2018) YOLOv3: an ıncremental ımprovement. arXiv:1804.02767v1. https://doi.org/10.48550/arXiv.1804.02767

  50. Wainberg M, Merico D, Delong A, Frey BJ (2018) Deep learning in biomedicine. Nat Biotechnol 36(9):829–838. https://doi.org/10.1038/nbt.4233

    Article  Google Scholar 

  51. Liu J, Li M, Lan W et al (2018) Classification of Alzheimer’s disease using whole brain hierarchical network. IEEE/ACM Trans Comput Biol Bioinform 15:624–632. https://doi.org/10.1109/TCBB.2016.2635144

    Article  Google Scholar 

  52. Ker J, Wang L, Rao J, Lim T (2017) Deep learning applications in medical image analysis. IEEE Access 6:9375–9379. https://doi.org/10.1109/ACCESS.2017.2788044

    Article  Google Scholar 

  53. Manzo M, Pellino S (2021) Voting in transfer learning system for ground-based cloud classification. Mach Learn Knowl Extr 3(3):542–553. https://doi.org/10.3390/make3030028

    Article  Google Scholar 

  54. Huang G, Liu Z, van der Maaten L, Weinberger KQ (2016) Densely connected convolutional networks. In: Proceedings - 30th IEEE conference on computer vision and pattern recognition, CVPR 2017 2017-Jan, pp 2261–2269. https://doi.org/10.48550/arxiv.1608.06993

  55. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for ımage recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 770–778

  56. He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. In: Lecture notes in computer science. pp 630–645

  57. Szegedy C, Wei Liu, Yangqing Jia, et al (2015) Going deeper with convolutions. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1–9

  58. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale ımage recognition. arXiv:1409.1556. https://doi.org/10.48550/arXiv.1409.1556

  59. Arshad H, Khan MA, Sharif MI, et al (2020) A multilevel paradigm for deep convolutional neural network features selection with an application to human gait recognition. Expert Syst 39(7): e12541. 1–21. https://doi.org/10.1111/exsy.12541

  60. Joshi VM, Ghongade RB, Joshi AM, Kulkarni RV (2022) Deep BiLSTM neural network model for emotion detection using cross-dataset approach. Biomed Signal Process Control 73:103407. https://doi.org/10.1016/J.BSPC.2021.103407

    Article  Google Scholar 

  61. Graves A (2013) Generating sequences with recurrent neural networks. arXiv:1308.0850. https://doi.org/10.48550/arxiv.1308.0850

  62. Uçar E, Atila Ü, Uçar M, Akyol K (2021) Automated detection of Covid-19 disease using deep fused features from chest radiography images. Biomed Signal Process Control 69:102862. https://doi.org/10.1016/J.BSPC.2021.102862

    Article  Google Scholar 

  63. Wei Y, Chen Z, Zhao C et al (2021) A BiLSTM hybrid model for ship roll multi-step forecasting based on decomposition and hyperparameter optimization. Ocean Eng 242:110138. https://doi.org/10.1016/J.OCEANENG.2021.110138

    Article  Google Scholar 

  64. Aktaş A, Doğan B, Demir Ö (2020) Tactile paving surface detection with deep learning methods. J Fac Eng Archit Gazi Univ 35:1685–1700. https://doi.org/10.17341/gazimmfd.652101

    Article  Google Scholar 

  65. An W, Wang H, Sun Q, et al (2018) A PID controller approach for stochastic optimization of deep networks. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 8522–8531. https://doi.org/10.1109/CVPR.2018.00889

  66. Maas AL, Qi P, Xie Z et al (2017) Building DNN acoustic models for large vocabulary speech recognition. Comput Speech Lang 41:195–213. https://doi.org/10.1016/j.csl.2016.06.007

    Article  Google Scholar 

  67. Ding S, Xu X, Nie R (2014) Extreme learning machine and its applications. Neural Comput Appl 25:549–556. https://doi.org/10.1007/S00521-013-1522-8/TABLES/1

    Article  Google Scholar 

  68. Zou Q, Qu K, Luo Y et al (2018) Predicting diabetes mellitus with machine learning techniques. Front Genet 9:515. https://doi.org/10.3389/fgene.2018.00515

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Cheng [46] to provide the public brain tumor dataset.

Funding

None.

Author information

Authors and Affiliations

  1. Faculty of Engineering, Software Engineering, Samsun University, Samsun, Turkey

    Abdulkadir Karacı

  2. Faculty of Engineering and Architecture, Computer Engineering, Kastamonu University, Kastamonu, Turkey

    Kemal Akyol

Authors
  1. Abdulkadir Karacı
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kemal Akyol
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kemal Akyol.

Ethics declarations

Conflict of interest

The authors declare that they have no conflicts 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

Karacı, A., Akyol, K. YoDenBi-NET: YOLO + DenseNet + Bi-LSTM-based hybrid deep learning model for brain tumor classification. Neural Comput & Applic 35, 12583–12598 (2023). https://doi.org/10.1007/s00521-023-08395-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-08395-2

Keywords

Search

Navigation