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Proceedings of the International Conference on Digital Manufacturing –
                                         Volume 2

               Huang, Jie, Yang, Guanghui, Li, Bo, He, Yu, & Liang, Yujun.
                    (2021). Segmentation of  cervical cell images based on
                    generative adversarial networks. IEEE Access, 9, 115415–
                    115428.
               Hussain, Ehtesham, Mahanta, Laba Bhuyan, Das, Chandan
                    Ranjan, & Talukdar, Ratul Kumar. (2020). A comprehensive
                    study on the multi-class cervical cancer diagnostic prediction
                    on pap smear images using  a fusion-based decision from
                    ensemble deep convolutional neural network. Tissue and
                    Cell, 65, Article 101347.
               Jahan, Shabnam, Afsana,  Fahmida, Ali,  Md. Amjad, Rahman,
                    Md.  Mahmudur, &  Miah, Md. Sajjad. (2021). Automated
                    invasive  cervical  cancer disease detection at early  stage
                    through suitable machine learning model. SN Applied
                    Sciences, 3(10), Article 748.
               Jia,  An  Dong,  Li,  Bo  Zhen,  &  Zhang,  Chang  Cheng. (2020).
                    Detection of cervical  cancer cells based on strong feature
                    CNN-SVM network. Neurocomputing, 411, 112–127.
               Kruczkowski, Mateusz, Drabik-Kruczkowska, Anna, Marciniak,
                    Agnieszka, Tarczewska,  Magdalena,  Kosowska, Marta, &
                    Szczerska, Małgorzata. (2022). Predictions of cervical cancer
                    identification by photonic method combined with machine
                    learning. Scientific Reports, 12(1), Article 15572.
               Lilhore, Umesh Kumar, Singh, Ajay Kumar, Verma, Pradeep
                    Kumar, Saini, Chander Shekhar, Tiwari, Ankur, & Gupta,
                    Deepak. (2022). Hybrid  model for detection of cervical
                    cancer using causal analysis and machine learning
                    techniques. Computational and Mathematical  Methods in
                    Medicine, 2022, Article 8084820.
               Meza Ramirez, Carlos A., Greenop, Martin, Almoshawah, Yaser
                    A., Martin Hirsch, Philip L., & Rehman, Ihtesham U. (2023).
                    Advancing  cervical  cancer  diagnosis  and  screening  with
                    spectroscopy and machine learning. Expert Review of
                    Molecular Diagnostics, 23(10), 823–836.
               Ming, Yu, Dong, Xia, Zhao, Jian, Chen, Zhiyu, Wang, Hao, &
                    Wu, Nan. (2022). Deep  learning-based  multimodal image
                    analysis for cervical cancer detection. Methods, 205, 46–52.







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