Abstract
Brain tumor classification is one of the challenging tasks in the field of medical image analysis. Any faults occurred during brain tumor diagnosis process may result in reduced life span of humans. Automating the process of tumor diagnosis may reduce the number of human errors in diagnosis process. With the recent technological advancements, visual attention for medical image analysis tasks has been widely employed by research community in designing computer-aided diagnosis systems. In this work, we introduce a multi-level attention mechanism for the task of brain tumor recognition. The proposed multi-level attention network (MANet) includes both spatial and cross-channel attention which not only focuses on prioritizing tumor region, but also maintains cross-channel temporal dependencies present in semantic feature sequence obtained from Xception backbone. The performance of proposed approach is evaluated on Figshare and BraTS benchmark datasets. Our experimental studies reveal that, cross-channel attention block along with spatial attention generalizes better and leads to superior performance with fewer model parameters. Our proposed MANet achieved a highest accuracy of 96.51% on Figshare and 94.91% on BraTS’2018 datasets and outperformed several existing models for the tumor recognition task.
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Abiwinanda, N., Hanif, M., Hesaputra, S. T., Handayani, A., Mengko, T. R.: Brain tumor classification using convolutional neural network. In: World Congress on Medical Physics and Biomedical Engineering 2018. Springer, pp. 183–189 (2019)
Afshar, P., Mohammadi, A., Plataniotis, K. N.: Brain tumor type classification via capsule networks. In: 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, pp. 3129–3133 (2018)
Arakeri, M.P., Reddy, G.R.M.: Computer-aided diagnosis system for tissue characterization of brain tumor on magnetic resonance images. Signal Image Video Process. 9(2), 409–425 (2015)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv:1409.0473v7 (2014)
Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R.T., Berger, C., Ha, S.M., Rozycki, M., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv preprint arXiv:1811.02629 (2018)
Bodapati, J.D., Shaik, N.S., Naralasetti, V.: Composite deep neural network with gated-attention mechanism for diabetic retinopathy severity classification. J. Ambient Intell. Humanized Comput. (2021). https://doi.org/10.1007/s12652-020-02727-z
Bodapati, J.D., Shaik, N.S., Naralasetti, V.: Deep convolution feature aggregation: an application to diabetic retinopathy severity level prediction. Signal Image Video Process. (2021). https://doi.org/10.1007/s11760-020-01816-y
Bodapati, J.D., Shaik, N.S., Naralasetti, V., Mundukur, N.B.: Joint training of two-channel deep neural network for brain tumor classification. Signal Image Video Process. (2020). https://doi.org/10.1007/s11760-020-01793-2
Bodapati, J.D., Shareef, S.N., Naralasetti, V., Mundukur, N.B.: Msenet: multi-modal squeeze-and-excitation network for brain tumor severity prediction. Int. J. Pattern Recognit. Artif. Intell. (2021). https://doi.org/10.1142/S0218001421570056
Bodapati, J.D., Vijay, A., Veeranjaneyulu, N.: Brain tumor detection using deep features in the latent space. Ingènierie des Systèmes d’Information 25, 259–265 (2020)
Chen, W., Liu, B., Peng, S., Sun, J., and Qiao, X.: S3d-unet: separable 3d u-net for brain tumor segmentation. In: International MICCAI Brainlesion Workshop, Springer, pp. 358–368 (2018)
Cheng, J.: Brain tumor dataset, 4 (2017)
Cheng, J., Huang, W., Cao, S., Yang, R., Yang, W., Yun, Z., Wang, Z., Feng, Q.: Enhanced performance of brain tumor classification via tumor region augmentation and partition. PloS one 10(10), e0140381 (2015)
Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
Deepak, S., Ameer, P.: Automated categorization of brain tumor from MRI using CNN features and SVM. J. Ambient Intell. Humanized Comput. (2020). https://doi.org/10.1007/s12652-020-02568-w
Deepika, K., Bodapati, J.D., Srihitha, R.K.: An efficient automatic brain tumor classification using LBP features and SVM-based classifier. In: Proceedings of international conference on computational intelligence and data engineering. Springer, pp. 163–170 (2019)
Fukui, H., Hirakawa, T., Yamashita, T., Fujiyoshi, H.: Attention branch network: learning of attention mechanism for visual explanation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Ghaffari, M., Sowmya, A., Oliver, R.: Automated brain tumor segmentation using multimodal brain scans: a survey based on models submitted to the brats 2012–2018 challenges. IEEE Rev. Biomed. Eng. 13, 156–168 (2019)
Khan, M.A., Ashraf, I., Alhaisoni, M., Damaševičius, R., Scherer, R., Rehman, A., Bukhari, S.A.C.: Multimodal brain tumor classification using deep learning and robust feature selection: a machine learning application for radiologists. Diagnostics 10(8), 565 (2020)
Luong, M.-T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. arXiv:1508.04025v5 (2015)
Mahesh, K.M., Renjit, J.A.: Evolutionary intelligence for brain tumor recognition from MRI images: a critical study and review. Evolut. Intell. 11(1–2), 19–30 (2018)
Pashaei, A., Sajedi, H., Jazayeri, N.: Brain tumor classification via convolutional neural network and extreme learning machines. In: 2018 8th International Conference on Computer and Knowledge Engineering (ICCKE), pp. 314–319 (2018)
Paul, J.S., Plassard, A.J., Landman, B.A., Fabbri, D.: (2017) Deep learning for brain tumor classification. In: Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging. International Society for Optics and Photonics, Vol. 10137, p. 1013710
Rehman, A., Naz, S., Razzak, M.I., Akram, F., Imran, M.: A deep learning-based framework for automatic brain tumors classification using transfer learning. Circuits Syst. Signal Process. 39(2), 757–775 (2020)
Sajjad, M., Khan, S., Muhammad, K., Wu, W., Ullah, A., Baik, S.W.: Multi-grade brain tumor classification using deep CNN with extensive data augmentation. J. Comput. Sci. 30, 174–182 (2019)
Sharif, M.I., Li, J.P., Khan, M.A., Saleem, M.A.: Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images. Pattern Recognit. Lett. 129, 181–189 (2020)
Xingjian, S.H.I., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.C.: Convolutional lstm network: a machine learning approach for precipitation nowcasting. In: Advances in neural information processing systems, Curran Associates, Inc., vol. 28, pp. 802–810 (2015)
Swati, Z.N.K., Zhao, Q., Kabir, M., Ali, F., Ali, Z., Ahmed, S., Lu, J.: Brain tumor classification for MR images using transfer learning and fine-tuning. Comput. Med. Imaging Gr. 75, 34–46 (2019)
Swati, Z.N.K., Zhao, Q., Kabir, M., Ali, F., Ali, Z., Ahmed, S., Lu, J.: Content-based brain tumor retrieval for MR images using transfer learning. IEEE Access 7, 17809–17822 (2019)
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R., Bengio, Y.: Show, attend and tell: neural image caption generation with visual attention. In: Proceedings of machine learning research (Lille, France, 09 2015). PMLR, vol. 37, pp. 2048–2057
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Shaik, N.S., Cherukuri, T.K. Multi-level attention network: application to brain tumor classification. SIViP 16, 817–824 (2022). https://doi.org/10.1007/s11760-021-02022-0
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DOI: https://doi.org/10.1007/s11760-021-02022-0