Pap smear-based cervical cancer detection using hybrid deep learning and performance evaluation

References

• Akpudo UE, Hur, J. W. J. 2021. D-dCNN: a novel hybrid deep learning-based tool for vibration-based diagnostics. Energies. 14(17):5286. doi:10.3390/en14175286.
   Google Scholar
• Basak H, Kundu R, Chakraborty S, Das N. 2021. Cervical cytology classification using PCA and GWO enhanced deep features selection. SN COMPUT SCI. 2.369. doi:10.1007/s42979-021-00741-2.
   Google Scholar
• Bayes T. 1763. “An essay towards solving a problem in the doctrine of chances,“philosophical Transactions (1683-1775). Vol. 53, pp. 370–418. ( 49 pages)
   Google Scholar
• Bedell SL, Goldstein, L. S., Goldstein, A. R., and Goldstein, A. T. 2020. Cervical cancer screening: past, present, and future. Sex Med Rev. 8(1): 28–37. January. 10.1016/j.sxmr.2019.09.005
   Google Scholar
• Bobdey S, Sathwara J, Jain A, Balasubramaniam G. 2016. Burden of cervical cancer and role of screening in India. Indian J Med Paediatr Oncol. 37.278–285. doi:10.4103/0971-5851.195751.
   Google Scholar
• Dong N, Zhao L, Wu CH, Chang JF. 2020. Inception v3 based cervical cell classification combined with artificially extracted features. Appl Soft Comput. 93.106311. doi:10.1016/j.asoc.2020.106311.
   Google Scholar
• He K, Zhang X, Ren S, Sun J. 2016. Deep residual learning for image recognition. In Conference on Computer Vision and Pattern Recognition (CVPR); Las Vegas, NV, USA: IEEE.
   Google Scholar
• Ho TK, 1995. Random decision forests. In Document analysis and recognition; Montreal, QC, Canada: IEEE.
   Google Scholar
• Jenkins D. 2007. Histopathology and cytopathology of cervical cancer. Dis Markers. 23:199–212. doi:10.1155/2007/874795.
   Google Scholar
• Krizhevsky A, Sutskever I, Hinton GE. 2017. ImageNet classification with deep convolutional. ACM. 60.84–90. doi:10.1145/3065386.
   Google Scholar
• LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature. 521.436–444. doi:10.1038/nature14539.
   Google Scholar
• Mahmood A, Ospina, A. G., Bennamoun, M., An, S., Sohel F., Boussaid FHovey R., Fisher, R. B. and Kendrick G. A. 2020. Automatic hierarchical classification of kelps using deep residual features. Sensors. 20.447. Januray. doi:10.3390/s20020447.
   Google Scholar
• Mane SS, Shinde SV. 2017. Different techniques for skin cancer detection using dermoscopy images. Int j comput sci eng technol. 5.159–163. doi:10.26438/ijcse/v5i12.159163.
   Google Scholar
• OrhanYaman T, 2022. Exemplar pyramid deep feature extraction based cervical cancer image classification model using pap-smear images. Biomedical Signal Processing and Control. 73. 103428. ISSN 1746-8094. doi : 10.1016/j.bspc.2021.103428.
   Google Scholar
• Plissiti ME et.al., 2018. Sipakmed: a new dataset for feature and image based classification of normal and pathological cervical cells in pap smear images. In IEEE International Conference on Image Processing (ICIP).
   Google Scholar
• Plissiti ME and Dimitrakopoulos P, 2015. Sipakmed: a new dataset for feature and image based classification of normal and pathological cervical cells in pap smear images; IEEE International Conference on Image Processing (ICIP): Athens, Greece.
   Google Scholar
• Rahaman M, Li C, Yao Y, Kulwa F, Wu X, Li X, Wang Q. 2021. DeepCervix: a deep learning-based framework for the classification of cervical cells using hybrid deep feature fusion techniques. Comput Biol Med. 136:104649. doi:10.1016/j.compbiomed.2021.104649.
   Google Scholar
• Sarwar A, Sharma V, Gupta R. 2015. Hybrid ensemble learning technique for screening of cervical cancer using Papanicolaou smear image analysis. Per Med Univ. 4.54–62. doi:10.1016/j.pmu.2014.10.001.
   Google Scholar
• Shraddha Deshmukh SS, 2016. Diagnosis of lung cancer using pruned fuzzy min-max neural network. In 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT); Pune, India.
   Google Scholar
• Szegedy C et.al., 2015. Going deeper with convolutions. In Conference on Computer Vision and Pattern Recognition (CVPR); Boston, MA: IEEE.
   Google Scholar
• Xiang Y, Sun W, Pan C, Yan M, Yin Z, Liang Y. 2020. A novel automation-assisted cervical cancer reading method based on convolutional neural network. Biocybern Biomed Eng. 40(2):611–623. doi:10.1016/j.bbe.2020.01.016.
   Google Scholar
• Zeyuan Song T, Zhen XC, Gao W & Zhu W (2022),“Identification of potential driving genes in prostatic cancer using complex network analysis”, In Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, DOI: 10.1080/21681163.2021.2015722
   Google Scholar
• Zhang L. 2017. DeepPap: deep convolutional networks for cervical cell classification. IEEE Journal of Biomedical and Health Informatics. doi:10.1109/JBHI.2017.2705583.
   Google Scholar