Abstract
Brain tumor grade detection is one of the perpetual tasks in brain image classification. Deep learning models are the most successful for multi-class classification which are trained for non-medical image classification. Thus, there is a need for re-training and feature enhancement for better performance in medical image classification. In this paper, we have proposed a residual multi-head attention network to uplift the re-training process with polished feature extraction. The proposed model consists of three parts including a pre-trained EfficientNetB4, a residual multi-head attention network, and a dense network. The residual multi-head attention network utilizes the attention block with three convolution layers for better tumor detection. The residual connection used in the network avoids the vanishing gradient problem. We have extracted a two-class (low-grade/high-grade) dataset from REMBRANDT repository. The proposed model has attained an accuracy of 96.39% and outperforms its competing models in vital metrics.
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
Abdallah, M.B., et al.: Data-driven predictive models of diffuse low-grade gliomas under chemotherapy. IEEE J. Biomed. Health Inform. 23(1), 38–46 (2018)
Chowdary, G.J., Punn, N.S., Sonbhadra, S.K., Agarwal, S.: Face mask detection using transfer learning of inceptionv3 (2020)
Clark, K., et al.: The cancer imaging archive (TCIA): maintaining and operating a public information repository. J. Digital Imaging 26(6), 1045–1057 (2013)
Ding, Y., et al.: Mvfusfra: a multi-view dynamic fusion framework for multimodal brain tumor segmentation. IEEE J. Biomed. Health Inform. 26(4), 1570–1581 (2021)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Howard, A.G., et al.: MobileNets: Efficient convolutional neural networks for mobile vision applications. (2017) arXiv preprint arXiv:1704.04861
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700–4708 (2017)
Irmak, E.: Multi-classification of brain tumor MRI images using deep convolutional neural network with fully optimized framework. Iranian J. Sci. Technol. Trans. Electr. Eng., pp. 1–22 (2021)
Isunuri, B.V., Kakarla, J.: Three-class brain tumor classification from magnetic resonance images using separable convolution based neural network. Concurrency Comput. Pract. Experience 34(1), e6541 (2022)
Elhoseny, M., et al.: Optimal feature level fusion based ANFIS classifier for brain MRI image classification. Concurrency and Computation: Practice Experience 32(1), e4887 (2020)
Kalpana, R., Chandrasekar, P.: An optimized technique for brain tumor classification and detection with radiation dosage calculation in MRI image. Microprocess. Microsyst. 72, 102903 (2020). https://doi.org/10.1016/j.micpro.2019.102903
Kaur, T., Saini, B.S., Gupta, S.: An optimal spectroscopic feature fusion strategy for MRI brain tumor classification using fisher criteria and parameter-free bat optimization algorithm. Biocybernetics Biomed. Eng. 38(2), 409–424 (2018)
Kumar, T.S., Arun, C., Ezhumalai, P.: An approach for brain tumor detection using optimal feature selection and optimized deep belief network. Biomed. Signal Process. Control 73, 103440 (2022)
Lu, S., Lu, Z., Zhang, Y.D.: Pathological brain detection based on AlexNet and transfer learning. J. Comput. Sci. 30, 41–47 (2019)
Nayak, D.R., Dash, R., Majhi, B., Wang, S.: Combining extreme learning machine with modified sine cosine algorithm for detection of pathological brain. Comput. Electr. Eng. 68, 366–380 (2018)
Sekhar, A., Biswas, S., Hazra, R., Sunaniya, A.K., Mukherjee, A., Yang, L.: Brain tumor classification using fine-tuned google net features and machine learning algorithms: IOMT enabled cad system. IEEE J. Biomed. Health Inform. 26(3), 983–991 (2021)
Szegedy, C., Sergey Ioffe, V.V., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. CoRR abs/1512.00567 (2015)
Tan, M., Le, Q.: Efficientnet: rethinking model scaling for convolutional neural networks. In: International conference on machine learning, pp. 6105–6114. PMLR (2019)
Tan, M., Le, Q.V.: Efficientnetv2: smaller models and faster training. CoRR abs/2104.00298 (2021). https://arxiv.org/abs/2104.00298
Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. process. Syst. 30 (2017)
Zhang, Y., et al.: Fractal dimension estimation for developing pathological brain detection system based on minkowski-bouligand method. IEEE Access 4, 5937–5947 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kakarla, J., Venkateswarlu, I.B. (2023). Brain Tumor Grade Detection Using Transfer Learning and Residual Multi-head Attention Network. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1777. Springer, Cham. https://doi.org/10.1007/978-3-031-31417-9_16
Download citation
DOI: https://doi.org/10.1007/978-3-031-31417-9_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-31416-2
Online ISBN: 978-3-031-31417-9
eBook Packages: Computer ScienceComputer Science (R0)