Abstract
A rapid increase in brain tumor cases mandates researchers for the automation of brain tumor detection and diagnosis. Multi-tumor brain image classification became a contemporary research task due to the diverse characteristics of tumors. Recently, deep neural networks are commonly used for medical image classification to assist neurologists. Vanishing gradient problem and overfitting are the demerits of the deep networks. In this paper, we have proposed a deep network model that uses ResNet-50 and global average pooling to resolve the vanishing gradient and overfitting problems. To evaluate the efficiency of the proposed model simulation has been carried out using a three-tumor brain magnetic resonance image dataset consisting of 3064 images. Key performance metrics have used to analyze the performance of the proposed model and its competitive models. We have achieved a mean accuracy of 97.08% and 97.48% with data augmentation and without data augmentation, respectively. Our proposed model outperforms existing models in classification accuracy.
Similar content being viewed by others
References
American Brain Tumor Association: Brain tumor education. https://www.abta.org/about-brain-tumors/brain-tumor-education/
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(1):63–74
Chahal PK, Pandey S, Goel S (2020) A survey on brain tumor detection techniques for mr images. Multimed Tools Appl
Cheng J, Yang W, Huang M, Huang W, Jiang J, Zhou Y (2016) Retrieval of brain tumors by adaptive spatial pooling and fisher vector representation. PLos ONE 11(6)
Cheng J Brain tumor dataset. https://figshare.com/articles/brain_tumor_dataset/1512427
Deepak S, Ameer PM (2019) Brain tumor classification using deep cnn features via transfer learning. Comput Biol Med 111:103345
El-Dahshan EA, Mohsen HM, Revett K, Salem AM (2014) Computer-aided diagnosis of human brain tumor through mri: A survey and a new algorithm. Expert Syst Appl 41(11):5526–5545
Frankly Speacking about Cancer: http://blog.braintumor.org/files/public-docs/frankly-speaking-about-cancer-brain-tumors.pdf
Gumaei A, Hassan MM, Hassan MR, Alelaiwi A, Fortino G (2019) A hybrid feature extraction method with regularized extreme learning machine for brain tumor classification. IEEE Access 7:36266–36273
He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition
Jude Hemanth D, Anitha J (2019) Modified genetic algorithm approaches for classification of abnormal magnetic resonance brain tumour images. Appl Soft Comput 75:21–28
Kaur T, Saini BS, Gupta S (2018) An optimal spectroscopic feature fusion strategy for mr brain tumor classification using fisher criteria and parameter-free bat optimization algorithm. Biocybernetics and Biomedical Engineering 38(2):409–424
Kumar Mallick P, Ryu SH, Satapathy SK, Mishra S, Nguyen GN, Tiwari P (2019) Brain mri image classification for cancer detection using deep wavelet autoencoder-based deep neural network. IEEE Access 7:46278–46287
Kunio D (2007) Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Comput Med Imaging Graph 31(4-5):198–211
Lee J (2019) Deep learning ensemble with data augmentation using a transcoder in visual description. Multimed Tools Appl 78:31231–31243
Lin M, Chen Q, Yan S (2013) Network in network
Lu S, Lu Z, Zhang YD (2019) Pathological brain detection based on alexnet and transfer learning. Journal of Computational Science 30:41–47
Nayak DR, Dash R, Chang X, Majhi B, Bakshi S (2018) Automated diagnosis of pathological brain using fast curvelet entropy features. IEEE Trans Sustain Comput
Radiopaedia: https://radiopaedia.org/articles
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. Journal of Computational Science 30:174–182
Swati ZNK, Zhao Q, Kabir M, Ali F, Ali Z, Ahmed S, Lu J (2019) Brain tumor classification for mr images using transfer learning and fine-tuning. Comput Med Imaging Graph 75:34–46
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Kumar, R.L., Kakarla, J., Isunuri, B.V. et al. Multi-class brain tumor classification using residual network and global average pooling. Multimed Tools Appl 80, 13429–13438 (2021). https://doi.org/10.1007/s11042-020-10335-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-10335-4