Federated Learning Framework for Brain Tumor Detection Using MRI Images in Non-IID Data Distributions

Nazir, M., Shakil, S., Khurshid, K.: Role of deep learning in brain tumor detection and classification (2015 to 2020): A review. Computerized Medical Imaging and Graphics 91, 101940 (2021). https://doi.org/10.1016/j.compmedimag.2021.101940

Article  PubMed  Google Scholar 

Zahin Muntaqim, M., Amir Smrity, T., Saleh Musa Miah, A., Muhammad Kafi, H., Tamanna, T., Farid, F.A., Abdur Rahim, M., Abdul Karim, H., Mansor, S.: Eye disease detection enhancement using a multi-stage deep learning approach. IEEE Access 12, 191393–191407 (2024). https://doi.org/10.1109/ACCESS.2024.3476412

Mammen, P.M.: Federated Learning: Opportunities and Challenges (2021). arXiv:2101.05428

McMahan, H.B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-Efficient Learning of Deep Networks from Decentralized Data (2023). arXiv:1602.05629

Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. In: Proceedings of Machine Learning and Systems, vol. 2, pp. 429–450 (2020)

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)

PubMed  PubMed Central  Google Scholar 

Amin, J., Sharif, M., Gul, N., Yasmin, M., Shad, S.A.: Brain tumor classification based on dwt fusion of mri sequences using convolutional neural network. Pattern Recognition Letters 129, 115–122 (2020)

Google Scholar 

Sharif, M., Tanvir, U., Munir, E.U., Khan, M.A., Yasmin, M.: Brain tumor segmentation and classification by improved binomial thresholding and multi-features selection. Journal of Ambient Intelligence and Humanized Computing, 1–20 (2018)

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, Systems, and Signal Processing 39(2), 757–775 (2020)

Google Scholar 

Ismael, M.R., Abdel-Qader, I.: Brain tumor classification via statistical features and back-propagation neural network. In: 2018 IEEE International Conference on Electro/Information Technology (EIT), pp. 0252–0257 (2018). IEEE

Aggarwal, M., Khullar, V., Goyal, N., Alammari, A., Albahar, M.A., Singh, A.: Lightweight federated learning for rice leaf disease classification using non independent and identically distributed images. Sustainability 15(16) (2023). https://doi.org/10.3390/su151612149

K, S., T, S., KS, P.: Advanced morphological technique for automatic brain tumor detection and evaluation of statistical parameters. Procedia Technology 24, 1374–1387 (2016)

MM, S., SK, S., V, S., S, E.: A non-invasive methodology for the grade identification of astrocytoma using image processing and artificial intelligence techniques. Expert Systems with Applications 43, 186–196 (2016)

E, A.-M., M, E., R, A.-A.: Brain tumor segmentation based on a hybrid clustering technique. Egyptian Informatics Journal 16(1), 71–81 (2015)

N, N., M, K.: Brain tumors detection and segmentation in mr images: Gabor wavelet vs. statistical features. Computers and Electrical Engineering 45, 286–301 (2015)

J, A., M, S., M, Y., SL, F.: A distinctive approach in brain tumor detection and classification using mri. Pattern Recognition Letters (2017)

L, Z., K, J.: Multiscale cnns for brain tumor segmentation and diagnosis. Computational and Mathematical Methods in Medicine (2016)

Zhao, L., Jia, K.: Deep feature learning with discrimination mechanism for brain tumor segmentation and diagnosis. In: 2015 International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), pp. 306–309 (2015). https://doi.org/10.1109/IIH-MSP.2015.41

El Boustani, A., El Bachari, E.: Mri brain images compression and classification using different classes of neural networks. In: Attiogbé, C., Ferrarotti, F., Maabout, S. (eds.) New Trends in Model and Data Engineering, pp. 122–134. Springer, Cham (2019)

Google Scholar 

Cheng, Y., Qin, G., Zhao, R., Liang, Y., Sun, M.: Convcaps: Multi-input capsule network for brain tumor classification. In: Gedeon, T., Wong, K.W., Lee, M. (eds.) Neural Information Processing, pp. 524–534. Springer, Cham (2019)

Google Scholar 

Liu, D., Liu, Y., Dong, L.: G-resnet: Improved resnet for brain tumor classification. In: Gedeon, T., Wong, K.W., Lee, M. (eds.) Neural Information Processing, pp. 535–545. Springer, Cham (2019)

Google Scholar 

Maharjan, S., Alsadoon, A., Prasad, P.W.C., Al-Dalain, T., Alsadoon, O.H.: A novel enhanced softmax loss function for brain tumour detection using deep learning. Journal of Neuroscience Methods 330, 108520 (2020). https://doi.org/10.1016/j.jneumeth.2019.108520

Article  PubMed  Google Scholar 

Joshi, S.R., Headley, D.B., Ho, K.C., Pare, D., Nair, S.S.: Classification of brainwaves using convolutional neural network. In: 2019 27th European Signal Processing Conference (EUSIPCO), pp. 1–5 (2019). https://doi.org/10.23919/EUSIPCO.2019.8902952

Ucuzal, H., YASAR, S., Colak, C.: Classification of brain tumor types by deep learning with convolutional neural network on magnetic resonance images using a developed web-based interface. In: 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 1–5 (2019). https://doi.org/10.1109/ISMSIT.2019.8932761

Adu, K., Yu, Y., Cai, J., Tashi, N.: Dilated capsule network for brain tumor type classification via mri segmented tumor region. In: 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 942–947 (2019). https://doi.org/10.1109/ROBIO49542.2019.8961610

Siar, M., Teshnehlab, M.: Brain tumor detection using deep neural network and machine learning algorithm. In: 2019 9th International Conference on Computer and Knowledge Engineering (ICCKE), pp. 363–368 (2019). https://doi.org/10.1109/ICCKE48569.2019.8964846

Han, C., Rundo, L., Araki, R., Nagano, Y., Furukawa, Y., Mauri, G., Nakayama, H., Hayashi, H.: Combining noise-to-image and image-to-image gans: Brain mr image augmentation for tumor detection. IEEE Access 7, 156966–156977 (2019). https://doi.org/10.1109/ACCESS.2019.2947606

Article  Google Scholar 

Li, M., Kuang, L., Xu, S., Sha, Z.: Brain tumor detection based on multimodal information fusion and convolutional neural network. IEEE Access 7, 180134–180146 (2019). https://doi.org/10.1109/ACCESS.2019.2958370

Article  Google Scholar 

Zhou, Y., Li, Z., Zhu, H., Chen, C., Gao, M., Xu, K., Xu, J.: Holistic brain tumor screening and classification based on densenet and recurrent neural network. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., Walsum, T. (eds.) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, pp. 208–217. Springer, Cham (2019)

Google Scholar 

Ozyurt, F., Sert, E., Avcı, D.: An expert system for brain tumor detection: Fuzzy c-means with super resolution and convolutional neural network with extreme learning machine. Medical Hypotheses 134, 109433 (2020). https://doi.org/10.1016/j.mehy.2019.109433

Article  PubMed  Google Scholar 

Comments (0)

No login
gif