Abstract
Many perspectives have been grown and extended instantaneously due to the evolution of the Fourth Industrial Revolution. Brain tumor detection is one of the most crucial mechanisms for standardization and care for injured patients. Early diagnosis from the beginning state lets the medical team develop comprehensive recovery protocols that help enhance patients’ survival rates. We have deployed the k-means clustering algorithm to stratify samples into three different view angles of MRI images (transverse, coronal, and sagittal) and combined a modified Residual Network (ResNet) architecture to diagnose three brain tumor types: glioma and meningioma pituitary tumor and recognize MRI images without tumor. The approach is evaluated on the dataset from Nanfang Hospital and General Hospital, Tianjin Medical University, China, with MRI images. Our result achieved 96% in brain tumor classification accuracy, the best among considered famous pre-trained networks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kathawala, F., Shah, A., Shah, J., Vora, S., Patil, S.: Brain tumor detection and classification. In: Sharma, H., Govindan, K., Poonia, R.C., Kumar, S., El-Medany, W.M. (eds.) Advances in Computing and Intelligent Systems. AIS, pp. 547–556. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-0222-4_52
Das, J., Ghosh, S., Chakraborty, R., Pramanik, A.: Deep learning based classification of brain tumor types from MRI scans. In: Nayak, J., Favorskaya, M.N., Jain, S., Naik, B., Mishra, M. (eds.) Advanced Machine Learning Approaches in Cancer Prognosis. ISRL, vol. 204, pp. 425–454. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-71975-3_16
Valentino, D.J., Mazziotta, J.C., Huang, H.K.: Visualization of human brain structure-function relationships. In: Images of the Twenty-First Century. Proceedings of the Annual International Engineering in Medicine and Biology Society, vol. 6, pp. 1737–1738 (1989)
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. In: Proceedings of the IEEE, vol. 86, pp. 2278–2324 (1998)
Selvapandian, A., Manivannan, K.: Fusion based glioma brain tumor detection and segmentation using ANFIS classification. Comput. Methods Programs Biomed. 166, 33–38 (2018)
Rajan, P.G., Sundar, C.: Brain tumor detection and segmentation by intensity adjustment. J. Med. Syst. 43(8), 1–13 (2019). https://doi.org/10.1007/s10916-019-1368-4
Khan, M.A., et al.: Brain tumor detection and classification: a framework of marker-based watershed algorithm and multilevel priority features selection. Microsc. Res. Tech. 82, 909–922 (2019)
Toğaçar, M., Ergen, B., Cömert, Z.: BrainMRNet: brain tumor detection using magnetic resonance images with a novel convolutional neural network model. Med. Hypotheses 134, 109531 (2020)
Sadad, T., Rehman, A., Munir, A., Saba, T., Tariq, U., Ayesha, N., Abbasi, R.: Brain tumor detection and multi-classification using advanced deep learning techniques. Microsc. Res. Tech. 84, 1296–1308 (2021)
Kim, S., Kim, B., Park, H.: Synthesis of brain tumor multicontrast MR images for improved data augmentation. Med. Phys. 48(5), 2185–2198 (2021). https://doi.org/10.1002/mp.14701
Tran, N.T., Tran, V.H., Nguyen, N.B., Nguyen, T.K., Cheung, N.M.: On data augmentation for GAN training. IEEE Trans. Image Process. 30, 1882–1897 (2021). https://doi.org/10.1109/TIP.2021.3049346
Luo, Y., Zhu, L.Z., Wan, Z.Y., Lu, B.L.: Data augmentation for enhancing EEG-based emotion recognition with deep generative models. J. Neural Eng. 17, 056021 (2020)
Sinaga, K.P., Yang, M.S.: Unsupervised k-means clustering algorithm. IEEE Access 8, 80716–80727 (2020). https://doi.org/10.1109/ACCESS.2020.2988796
Ke, X., Zou, J., Niu, Y.: End-to-end automatic image annotation based on deep CNN and multi-label data augmentation. IEEE Trans. Multimedia 21(8), 2093–2106 (2019). https://doi.org/10.1109/TMM.2019.2895511
Sai Sundar, K.V., Bonta, L.R., Reddy, A.K., Baruah, P.K., Sankara, S.S.: Evaluating training time of inception-v3 and Resnet-50, 101 models using tensorflow across CPU and GPU. In: 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 1964–1968 (2018)
Cheng, J.: Brain tumor dataset. Figshare (2017)
Kaplan, K., Kaya, Y., Kuncan, M., Ertunç, H.M.: Brain tumor classification using modified local binary patterns (LBP) feature extraction methods. Med. Hypotheses 139, 109696 (2020)
Waghmare, V.K., Kolekar, M.H.: Brain tumor classification using deep learning, pp. 155–175 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Nguyen, H.T. et al. (2022). Brain Tumors Detection on MRI Images with K-means Clustering and Residual Networks. In: Bădică, C., Treur, J., Benslimane, D., Hnatkowska, B., Krótkiewicz, M. (eds) Advances in Computational Collective Intelligence. ICCCI 2022. Communications in Computer and Information Science, vol 1653. Springer, Cham. https://doi.org/10.1007/978-3-031-16210-7_26
Download citation
DOI: https://doi.org/10.1007/978-3-031-16210-7_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16209-1
Online ISBN: 978-3-031-16210-7
eBook Packages: Computer ScienceComputer Science (R0)