Classification of Cervical Cancer Using an Autoencoder and Cascaded Multilayer Perceptron

References

• E. Ahishakiye, W. Mwangi, P. Muthoni, L. Nderu, and R. Wario, “Comparative performance of machine leaning algorithms in prediction of cervical cancer,” in 2021 IST-Africa Conference (IST-Africa), IEEE, 2021, pp. 1–13.
   Google Scholar
• F. Asadi, C. Salehnasab, and L. Ajori, “Supervised algorithms of machine learning for the prediction of cervical cancer,” J. Biomed. Phys. Eng., Vol. 10, no. 4, pp. 513, 2020.
   PubMedGoogle Scholar
• M. A. Haque, I. J. Dristy, S. Sharar, and A. A. Rasel, “ML classifier comparative performance analysis of prediction on cervical cancer,” in 2021 International Conference on Electronics, Communications and Information Technology (ICECIT), IEEE, 2021, pp. 1–6.
   Google Scholar
• M. M. Ali, K. Ahmed, F. M. Bui, B. K. Paul, S. M. Ibrahim, J. M. Quinn, and M. A. Moni, “Machine learning-based statistical analysis for early-stage detection of cervical cancer,” Comput. Biol. Med., Vol. 139, pp. 104985, 2021.
   PubMed Web of Science ®Google Scholar
• S. K. Singh, and A. Goyal, “Performance analysis of machine learning algorithms for cervical cancer detection,” Int. J. Healthc Inform. Sys. Informa (IJHISI), Vol. 15, no. 2, pp. 1–21, 2020.
   Web of Science ®Google Scholar
• N. Razali, S. A. Mostafa, A. Mustapha, M. H. Abd Wahab, and N. A. Ibrahim, “Risk factors of cervical cancer using classification in data mining,” J. Phys. Confer. Ser. IOP Publ., Vol. 1529, no. 2, pp. 022102, 2020.
   Google Scholar
• A. Castanon, R. Landy, F. Pesola, P. Windridge, and P. Sasieni, “Prediction of cervical cancer incidence in England, UK, up to 2040, under four scenarios: a modelling study,” Lancet Public Health, Vol. 3, no. 1, pp. e34–e43, 2018.
   PubMed Web of Science ®Google Scholar
• C. W. Zhang, D. Y. Jia, N. K. Wu, Z. G. Guo, and H. R. Ge, “Quantitative detection of cervical cancer based on time series information from smear images,” Appl. Soft. Comput., Vol. 112, pp. 107791, 2021.
   Web of Science ®Google Scholar
• W. William, A. Ware, A. H. Basaza-Ejiri, and J. Obungoloch, “A review of image analysis and machine learning techniques for automated cervical cancer screening from pap-smear images,” Comput. Methods Programs Biomed., Vol. 164, pp. 15–22, 2018.
   PubMed Web of Science ®Google Scholar
• V. Mishra, S. Aslan, and M. M. Asem, “Theoretical assessment of cervical cancer using machine learning methods based on Pap-smear test,” in 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), IEEE, 2018, November, pp. 1367–73.
   Google Scholar
• T. Pinto, and Y. Sebastian, “2021 IEEE madras section conference (MASCON),” in Detecting DDoS Attacks Using a Cascade of Machine Learning Classifiers Based on Random Forest and MLP-ANN, IEEE, 2021, August, pp. 1–6.
   Google Scholar
• E. A. Budu, V. L. Narasimhan, and Z. A. Mbero, “Sensitivity analysis of a multilayer perceptron network for cervical cancer risk classification,” in Data Science and Security, Springer, Singapore, 2021, pp. 80–8.
   Google Scholar
• S. Gupta, and M. Kumar, “Prostate cancer prognosis using multi-layer perceptron and class balancing techniques,” in 2021 Thirteenth International Conference on Contemporary Computing (IC3-2021), 2021, pp. 1–6.
   Google Scholar
• A. Tiwari, S. Tripathi, D. C. Pandey, N. Sharma, and S. Sharma, “Detection of COVID-19 infection in CT and X-ray images using transfer learning approach,” Technol. Health Care, Vol. 31, pp. 1–14, 2022.
   Web of Science ®Google Scholar
• S. V. Renuka, D. R. Edla, and J. Joseph, “An objective measure for assessing the quality of contrast enhancement on magnetic resonance images,” J. King Saud Univ.-Comput. Inf Sci., 2021.
   Web of Science ®Google Scholar
• M. Kaushik, R. C. Joshi, A. S. Kushwah, M. K. Gupta, M. Banerjee, R. Burget, and M. K. Dutta, “Cytokine gene variants and socio-demographic characteristics as predictors of cervical cancer: A machine learning approach,” Comput. Biol. Med., Vol. 134, pp. 104559, 2021.
   PubMed Web of Science ®Google Scholar
• A. Gupta, A. Anand, and Y. Hasija, “Recall-based machine learning approach for early detection of cervical cancer,” in 2021 6th International Conference for Convergence in Technology (I2CT), IEEE, 2021, pp. 1–5.
   Google Scholar
• L. Akter, M. M. Islam, M. S. Al-Rakhami, and M. R. Haque, “Prediction of cervical cancer from behavior risk using machine learning techniques,” SN Comput. Sci., Vol. 2, no. 3, pp. 1–10, 2021.
   Google Scholar
• S. Tripathi, T. S. Sharan, S. Sharma, and N. Sharma, “An augmented deep learning network with noise suppression feature for efficient segmentation of magnetic resonance images,” IETE Tech. Rev., 1–14, 2021.
   Web of Science ®Google Scholar
• Y. Wu, et al., “Fast and automated segmentation for the three-directional multi-slice cine myocardial velocity mapping,” Diagnostics, Vol. 11, no. 2, pp. 346, 2021.
   Web of Science ®Google Scholar
• S. Tripathi, and N. Sharma, “Computer-aided automatic approach for denoising of magnetic resonance images,” Comput. Methods Biomech. Biomed. Eng. Imaging. Vis., Vol. 9, no. 6, pp. 707–16, 2021.
   Web of Science ®Google Scholar
• S. Tripathi, and N. Sharma, “Computer-Based segmentation of cancerous tissues in biomedical images using enhanced deep learning model,” IETE Tech. Rev., 1–15, 2021.
   Web of Science ®Google Scholar
• W. Chen, X. Li, L. Gao, and W. Shen, “Improving computer-aided cervical cells classification using transfer learning-based snapshot ensemble,” Appl. Sci., Vol. 10, no. 20, pp. 7292, 2020.
   Google Scholar
• R. Weegar, and K. Sundström, “Using machine learning for predicting cervical cancer from Swedish electronic health records by mining hierarchical representations,” Plos one, Vol. 15, no. 8, pp. e0237911,  1–19, 2020.
   Web of Science ®Google Scholar
• X. Zhou, et al., “Systematic and comprehensive automated ventricle segmentation on ventricle images of the elderly patients: a retrospective study,” Front. Aging. Neurosci., Vol. 12, pp. 461, 2020.
   Web of Science ®Google Scholar
• H. Lin, Y. Hu, S. Chen, J. Yao, and L. Zhang, “Fine-grained classification of cervical cells using morphological and appearance based convolutional neural networks,” IEEE. Access, Vol. 7, pp. 71541–9, 2019.
   Web of Science ®Google Scholar
• L. D. Nguyen, D. Lin, Z. Lin, and J. Cao, “Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation,” in 2018 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE, 2018, pp. 1–5.
   Google Scholar
• T. Vigneswari, N. Vijaya, and N. Kalaiselvi, “early prediction of cervical cancer using machine learning techniques,” Turk. J. Physiother. Rehabil., Vol. 32, pp. 3.
   Google Scholar
• M. Sharma, “Cervical cancer prognosis using genetic algorithm and adaptive boosting approach,” Health. Technol., Vol. 9, no. 5, pp. 877–86, 2019.
   Web of Science ®Google Scholar
• A. Shetty, and V. Shah, “Survey of cervical cancer prediction using machine learning: A comparative approach,” in 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE, 2018, pp. 1–6.
   Google Scholar