A Study on Ensemble Learning for Cervical Cytology Classification
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[1] Ali, M.M., et al.: Machine learning-based statistical analysis for early stage detection of cervical cancer. Comput. Biol. Med. 139, 104985 (2021)
[2] Arbyn, M., et al.: Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis. Lancet Glob. Health 8(2), e191–e203 (2020)
[3] Dong, N., Zhao, L., Wu, C.H., Chang, J.F.: Inception V3 based cervical cell classification combined with artificially extracted features. ASC 93, 106311 (2020)
[4] Ganaie, M.A., Hu, M., Malik, A.K., Tanveer, M., Suganthan, P.N.: Ensemble deep learning: a review. Eng. Appl. AI 115, 105151 (2022)
[5] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on CVPR, pp. 770–778 (2016)
[6] 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)
[7] Kessler, T.A.: Cervical cancer: prevention and early detection. In: Seminars in Oncology Nursing, vol. 33, pp. 172–183. Elsevier (2017)
[8] Lee, K., Zung, J., Li, P., Jain, V., Seung, H.S.: Superhuman accuracy on the snemi3d connectomics challenge. arXiv preprint arXiv:1706.00120 (2017)
[9] Lever, J., Krzywinski, M., Altman, N.: Classification evaluation. Nature (2016)
[10] Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
[11] Mathivanan, S.K., Francis, D., Srinivasan, S., Khatavkar, V., P, K., Shah, M.A.: Enhancing cervical cancer detection and robust classification through a fusion of deep learning models. Sci. Rep. 14(1), 10812 (2024)
[12] Pramanik, R., Biswas, M., Sen, S., de Souza Júnior, L.A., Papa, J.P., Sarkar, R.: A fuzzy distance-based ensemble of deep models for cervical cancer detection. Comput. Methods Programs Biomed. 219, 106776 (2022)
[13] Rahaman, M.M., Li, C., Yao, Y., Kulwa, F., Wu, X., Li, X., Wang, Q.: Deepcervix: a deep learning-based framework for the classification of cervical cells using hybrid deep feature fusion techniques. Comput. Biol. Med. (2021)
[14] Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115, 211–252 (2015)
[15] Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)
[16] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
[17] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision (2016)
[18] Tan, M., Le, Q.: Efficientnet: rethinking model scaling for convolutional neural networks. In: ICML, pp. 6105–6114. PMLR (2019)
[19] Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: CVPR, pp. 1492–1500 (2017)
[20] Zhao, Z., Alzubaidi, L., Zhang, J., Duan, Y., Gu, Y.: A comparison review of transfer learning and self-supervised learning: definitions, applications, advantages and limitations. Expert Syst. Appl. 242, 122807 (2024)