861
Views
1
CrossRef citations to date
0
Altmetric
COMPUTER SCIENCE

Analyzing the performances of squash functions in capsnets on complex images

, &
Article: 2203890 | Received 23 Feb 2023, Accepted 13 Apr 2023, Published online: 23 Apr 2023

References

  • Afriyie, Y., Weyori, B. A., & Opoku, A. A. (2021, November). Exploring optimised capsule network on complex images for medical diagnosis. In 2021 IEEE 8th International Conference on Adaptive Science and Technology (ICAST) (pp. 1–14). IEEE.
  • Ayidzoe, M. A., Yongbin, Y., Kwabena, P., Jingye, M., Kwabena, C., & Yifan, A. (2021). Gabor capsule network with preprocessing blocks for the recognition of complex images. Machine Vision and Applications, 123. https://doi.org/10.1007/s00138-021-01221-6
  • Ding, X., Yong, L., Yang, J., Huapeng, L., Liu, L., Liu, Y., & Zhang, C. (2021). An adaptive capsule network for hyperspectral remote sensing classification. Remote Sensing, 13(13), 13. https://doi.org/10.3390/rs13132445
  • Edgar, X., Bing, S., & Jin, Y. (2017). Capsule Network Performance on Complex Data. arXiv preprint arXiv:171203480, 10707(Fall), 1–7. http://arxiv.org/abs/1712.03480
  • García-Alonso, C. R., Pérez-Naranjo, L. M., & Fernández-Caballero, J. C. (2014). Multiobjective evolutionary algorithms to identify highly autocorrelated areas: The case of spatial distribution in financially compromised farms. Annals of Operations Research, 219(1), 187–202. https://doi.org/10.1007/s10479-011-0841-3
  • Goceri, E. (2020). CapsNet topology to classify tumours from brain images and comparative evaluation. IET Image Processing, 14(5), 882–889. https://doi.org/10.1049/iet-ipr.2019.0312
  • Hajian-Tilaki, K. (2013). Receiver operating characteristic (roc) curve analysis for medical diagnostic test evaluation. Caspian Journal of Internal Medicine, 4(2), 627–635.
  • Harilal, N., & Patil, R. 2022. “Effectiveness of the recent advances in capsule networks.” https://arxiv.org/abs/2210.05834v1.
  • Hinton, G., Sabour, S., & Frosst, N. (2018). Matrix capsules with em routing. 1–15.
  • Kim, Y., Wang, P., Zhu, Y., & Mihaylova, L. (n.d.). A capsule network for traffic speed prediction in complex road networks.
  • Kwabena, P. M., Asubam Weyori, B., & Abra, A. (2020). Gabor capsule network for plant disease detection. International Journal of Advanced Computer Science and Applications, 11(10), 388–395. https://doi.org/10.14569/IJACSA.2020.0111048
  • LaLonde, R., & Bagci, U. (2018). Capsules for Object Segmentation. No Midl, 1–9. http://arxiv.org/abs/1804.04241
  • Lian, Y., Gu, D., & Hua, J. (2023). Sorcnet: robust non-rigid shape correspondence with enhanced descriptors by shared optimized res-CapsuleNet. The Visual Computer, 39(2), 749–763. https://doi.org/10.1007/s00371-021-02372-3
  • Marchisio, A., Bussolino, B., Colucci, A., Abdullah Hanif, M., Martina, M., Masera, G., & Shafique, M. 2020. “FasTrCaps: an integrated framework for fast yet accurate training of capsule networks.” Proceedings of the International Joint Conference on Neural Networks, no. July. https://doi.org/10.1109/IJCNN48605.2020.9207533.
  • Martins, V., Borin, E., Breternitz, M., & Capsnet, A. (2019). The multi-lane capsule network. IEEE Signal Processing Letters, 26(7), 1006–1010. https://doi.org/10.1109/LSP.2019.2915661
  • Mukhometzianov, R., & Carrillo, J. (n.d.). CapsNet comparative performance evaluation for image classification. 1–14.
  • Nair, P., Doshi, R., & Keselj, S. 2021. “Pushing the limits of capsule networks,” 1–16. http://arxiv.org/abs/2103.08074.
  • Neill, J. O. 2018. “Siamese capsule networks,” 1–10. http://arxiv.org/abs/1805.07242.
  • Rawlinson, D., Ahmed, A., & Kowadlo, G. 2018. “Sparse unsupervised capsules generalize better.” http://arxiv.org/abs/1804.06094.
  • Sara, S., Frosst, N., & Hinton, G. E. (2017). Dynamic routing between capsules. Advances in Neural Information Processing Systems, 30( 2017-Decem), 3857–3867.
  • Steur, N. A. K., & Schwenker, F. (2021). Next-generation neural networks: capsule networks with routing-by-agreement for text classification. IEEE Access, 9, 125269–125299. https://doi.org/10.1109/ACCESS.2021.3110911
  • Sun, K., Wen, X., Yuan, L., & Haixia, X. (2021). Dense capsule networks with fewer parameters. Soft Computing, 25(10), 6927–6945. https://doi.org/10.1007/s00500-021-05774-6
  • Tiwari, S. (2021). Dermatoscopy using multi-layer perceptron, convolution neural network, and capsule network to differentiate malignant melanoma from benign nevus. International Journal of Healthcare Information Systems and Informatics, 16(3), 58–73. https://doi.org/10.4018/IJHISI.20210701.oa4
  • Tiwari, S., & Jain, A. (2021). Convolutional capsule network for COVID-19 detection using radiography images. International Journal of Imaging Systems and Technology, 31(2), 525–539. https://doi.org/10.1002/ima.22566
  • Using, N., & Training, L. (n.d.). Hyperspectral image classification with capsule network using limited training samples. Sensors, 18(9), 3153 https://doi.org/10.3390/s18093153
  • Wang, D., & Liu, Q. (2018). An optimization view on dynamic routing between capsules. Proceedings of the 6th International Conference on Learning Representations, ICLR 2018 - Workshop Track Proceedings, Vancouver Convention Center, Vancouver, BC, Canada, 1–4.
  • Xiong, Y., Guiping, S., Shiwe, Y., Sun, Y., & Sun, Y. (2019) Deeper Capsule network for complex data. July, 1–8.
  • Yaw, A., Weyori, B. A., & Opoku, A. A. (2022a). Classification of blood cells using optimized Capsule networks. Neural Processing Letters. https://doi.org/10.2139/ssrn.4073627
  • Yaw, A., Weyori, B. A., & Opoku, A. A. (2022b). Classification of blood cells using optimized Capsule networks. Neural Processing Letters. https://doi.org/10.2139/ssrn.4073627/
  • Yaw, A., Weyori, B. A., & Opoku, A. A. (2022c). Gastrointestinal tract disease recognition based on denoising Capsule network. Cogent Engineering, 9(1), 0–17. https://doi.org/10.1080/23311916.2022.2142072
  • Zhang, X., Pengshuai, L., Jia, W., & Zhao, H. (2017). Multi-labeled relation extraction with attentive Capsule network.
  • Zhao, Y., & Cen, Y. 2014. “Data mining applications with R.” 2014. http://proquest.safaribooksonline.com.proxy1.library.mcgill.ca/book/programming/r/978%0A184%0A0124115118.