Federated Texture Classification: Implementing Colorectal Histology Image Analysis using Federated Learning
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Abstract
This research explores neural network models' performance and adaptability in the context of the colorectal histology dataset as it pertains to the categorization of textures. Inception, VGG19, and MobileNet, together with their federated variations, are among the models being examined. The study includes a detailed evaluation, parameter analysis, and training information. VGG19 stands out as a particularly noteworthy high performance, with remarkable accuracy, precision, and recall. Due to its lightweight design, MobileNet performs less well, but its potential is enhanced by the addition of federated learning. The accuracy and precision of federated versions of Inception, VGG19, MobileNet, and a Lightweight MobileNet model are competitive, with FL-Lightweight MobileNet achieving outstanding results. The work has important ramifications for the field of medical image analysis since it shows how federated learning may balance the need for data confidentiality and privacy with model performance. This study marks a turning point in the development of medical imaging by opening the door to in-depth investigation into the complex interactions across federated paradigms. Furthermore, these results provide a compelling story in the wider discussion of how cutting-edge technologies and the pressing needs of contemporary healthcare might work together.
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References
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data. In Artificial Intelligence and Statistics (AISTATS).
Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016). Federated Learning: Strategies for Improving Communication Efficiency. In NeurIPS Workshop on Private Multi-Party Machine Learning.
Li, C., Smith, A., & Talwalkar, A. (2019). Federated Learning in Medicine: Practical Frameworks and Challenges. Proceedings of Machine Learning Research, 97, 3978-3987.
He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Image Recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
S. Ajani and M. Wanjari, "An Efficient Approach for Clustering Uncertain Data Mining Based on Hash Indexing and Voronoi Clustering," 2013 5th International Conference and Computational Intelligence and Communication Networks, 2013, pp. 486-490, doi: 10.1109/CICN.2013.106.
Khetani, V. ., Gandhi, Y. ., Bhattacharya, S. ., Ajani, S. N. ., & Limkar, S. . (2023). Cross-Domain Analysis of ML and DL: Evaluating their Impact in Diverse Domains. International Journal of Intelligent Systems and Applications in Engineering, 11(7s), 253–262. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2951
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. In Medical Image Computing and Computer-Assisted Intervention.
Bayramoglu, N., Kannala, J., & Heikkilä, J. (2016). Deep Learning for Magnification Independent Cell Nucleus Detection. In IEEE Transactions on Medical Imaging.
Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., ... & Shroyer, K. R. (2018). Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. In Cell Reports.
Yang, J., Zhang, D., & Frangi, A. F. (2008). Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence.
Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv preprint arXiv:1409.1556.
Ojala, T., Pietikäinen, M., & Harwood, D. (2002). A Comparative Study of Texture Measures with Classification Based on Featured Distributions. Pattern Recognition.
McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2016). Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics.
Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016). Federated Learning: Strategies for Improving Communication Efficiency. arXiv preprint arXiv:1610.05492.
Li, C., Smith, A., & Talwalkar, A. (2020). Federated Learning in Medicine: Practical Frameworks and Challenges. arXiv preprint arXiv:2002.08597.
Zhang, Y., Xie, S., & Ji, X. (2021). Federated Learning for Breast Density Classification: A Real-World Implementation. In International Conference on Medical Imaging with Deep Learning.
Gupta, R., Kuznetsova, I., & Kambhamettu, C. (2016). Contextual Inference in Microscopy Images. In Medical Image Computing and Computer-Assisted Intervention (MICCAI).
Yuan, Y., & Wang, Y. (2021). Federated Learning for Healthcare: Challenges, Opportunities, and Future Directions. IEEE Journal of Biomedical and Health Informatics, 25(8), 2941-2952.
Shen, D., Wu, G., & Suk, H. I. (2017). Deep Learning in Medical Image Analysis. Annual Review of Biomedical Engineering, 19, 221-248.
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely Connected Convolutional Networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Goh, G. B., Hodas, N. O., & Vishnu, A. (2017). Deep Learning for Computational Chemistry. Journal of Computational Chemistry, 38(16), 1291-1307.
Shao, L., & Zhu, X. (2014). Real-Time Computerized Detection of Abnormalities in Endoscopic Images. IEEE Transactions on Biomedical Engineering, 61(10), 2776-2783.
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017). ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., & Pal, C. (2017). Brain Tumor Segmentation with Deep Neural Networks. Medical Image Analysis, 35, 18-31.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems (NeurIPS).
Nand Kishor Gupta, Dr. Bharat Bhushan Jain, Ashish Raj, & Satish Kumar Alaria. (2022). Numerical Simulation and Design of Power Fluctuation Controlling of Hybrid Energy Storage System Based on Modified Particle Swarm Optimization. International Journal on Recent Technologies in Mechanical and Electrical Engineering, 9(3), 88–95. https://doi.org/10.17762/ijrmee.v9i3.378
Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 115(3), 211-252.
Chollet, F. (2017). Xception: Deep Learning with Depthwise Separable Convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Bejnordi, B. E., Veta, M., van Diest, P. J., van Ginneken, B., Karssemeijer, N., Litjens, G., ... & Ciompi, F. (2017). Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women with Breast Cancer. JAMA, 318(22), 2199-2210.