[1] Desiani, A., Suprihatin, B., Yahdin, S., Putri, A. I., & Husein, F. R. (2021). Bi-path Architecture of CNN Segmentation and Classification Method for Cervical Cancer Disorders Based on Pap-smear Images.
International Association of Engineers International Journal of Computer Science,
48(3), 782-791.
https://www.iaeng.org/ IJCS/issues_v48/issue_3/IJCS_48_3_37.pdf
[2] Martínez-Más, J., Bueno-Crespo, A., Martínez-España, R., Remezal-Solano, M., Ortiz-González, A., Ortiz-Reina, S., & Martínez-Cendán, J.-P. (2020). Classifying Papanicolaou cervical smears through a cell merger approach by deep learning technique.
Expert Systems with Applications,
160, 113707.
https://doi.org/10.1016/ j.eswa.2020.113707
[3] Sahba, F., & Tizhoosh, H. R. (2003, May 04-07).
Filter fusion for image enhancement using reinforcement learning [Conference session]
. Canadian Conference on Electrical and Computer Engineering, Montreal, Québec, Canada.
https://doi.org/1 0.1109/CCECE.2003.1226027
[4] Sompawong, N., Mopan, J., Pooprasert, P., Himakhun, W., Suwannarurk, K., Ngamvirojcharoen, J., Vachiramon, T., & Tantibundhit, C. (2019, July 23-27).
Automated Pap Smear Cervical Cancer Screening Using Deep Learning [Conference session]
. 2019 41st Annual International Conference of the Institute of Electrical and Electronics Engineers Engineering in Medicine and Biology Society, Berlin, Germany.
https://doi.org/10.1109/EMBC.2019.8856369
[5] Yaman, O., & Tuncer, T. (2022). Exemplar pyramid deep feature extraction based cervical cancer image classification model using pap-smear images.
Biomedical Signal Processing and Control,
73, 103428.
https://doi.org/10.1016/j.bspc.2021.103428
[6] Lee, H., & Kim, J. (2016, June 26- July 1).
Segmentation of Overlapping Cervical Cells in Microscopic Images with Superpixel Partitioning and Cell-Wise Contour Refinement [Conference session]
. 2016 Institute of Electrical and Electronics Engineers Conference on Computer Vision and Pattern Recognition Workshops, Las Vegas, Nevada, USA.
https://doi.org/10.1109/CVPRW.2016.172
[7] Lu, Z., Carneiro, G., Bradley, A. P., Ushizima, D., Nosrati, M. S., Bianchi, A. G. C., Carneiro, C. M., & Hamarneh, G. (2017). Evaluation of Three Algorithms for the Segmentation of Overlapping Cervical Cells.
Institute of Electrical and Electronics Engineers Journal of Biomedical and Health Informatics,
21(2), 441-450.
https:// doi.org/10.1109/JBHI.2016.2519686
[8] Nosrati, M. S., & Hamarneh, G. (2015, April 16-19).
Segmentation of overlapping cervical cells: A variational method with star-shape prior [Conference session]
. 12th International Symposium on Biomedical Imaging, Brooklyn, New York , USA.
https://doi.org/10.1109/ISBI.2015.7163846
[9] Plissiti, M. E., Nikou, C., & Charchanti, A. (2011). Automated Detection of Cell Nuclei in Pap Smear Images Using Morphological Reconstruction and Clustering.
Institute of Electrical and Electronics Engineers Transactions on Information Technology in Biomedicine,
15(2), 233-241.
https://doi.org/10.1109/TITB.2010.2087030
[10] Araújo, F. H. D., Silva, R. R. V., Ushizima, D. M., Rezende, M. T., Carneiro, C. M., Campos Bianchi, A. G., & Medeiros, F. N. S. (2019). Deep learning for cell image segmentation and ranking.
Computerized Medical Imaging and Graphics,
72, 13-21.
https://doi.org/10.1016/j.compmedimag.2019.01.003
[11] Subhi Al-batah, M., Mat Isa, N. A., Klaib, M. F., & Al-Betar, M. A. (2014). Multiple Adaptive Neuro-Fuzzy Inference System with Automatic Features Extraction Algorithm for Cervical Cancer Recognition.
Computational and Mathematical Methods in Medicine,
2014(1), 181245.
https://doi.org/10.1155/2014/181245
[12] Bora, K., Chowdhury, M., Mahanta, L. B., Kundu, M. K., & Das, A. K. (2016, December 18 - 22).
Pap smear image classification using convolutional neural network [Conference session]
. Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing, Guwahati, Assam, India.
https://doi.org/10 .1145/3009977.3010068
[13] Wu, M., Yan, C., Liu, H., Liu, Q., & Yin, Y. (2018). Automatic classification of cervical cancer from cytological images by using convolutional neural network.
Bioscience Reports,
38(6), 1-9.
https://doi.org/10.1042/bsr20181769
[14] Harangi, B., Toth, J., Bogacsovics, G., Kupas, D., Kovacs, L., & Hajdu, A. (2019, September 23-25).
Cell detection on digitized Pap smear images using ensemble of conventional image processing and deep learning techniques [Conference session]
. 11th International Symposium on Image and Signal Processing and Analysis Dubrovnik, Croatia.
https://doi.org/10.1109/ISPA.2019.8868683
[15] Zhang, L., Le, L., Nogues, I., Summers, R. M., Liu, S., & Yao, J. (2017). DeepPap: Deep Convolutional Networks for Cervical Cell Classification.
Institute of Electrical and Electronics Engineers Journal of Biomedical and Health Informatics,
21(6), 1633-1643.
https://doi.org/10.1109/JBHI.2017.2705583
[16] Dharani, C., Kaviya, S., Maheshwari, S., Monisha, K., & Elayaraja, P. (2020). Visualization of cervical cancer classification using deep convolutional neural network.
International Journal of New Innovations in Engineering and Technology, (Special Issue), 196-206.
http://www.ijniet.org/wp-content/uploads/2020/08/s29.pdf
[17] Waly, M.-I., Sikkandar, M.-Y., Aboamer, M.-A., Kadry, S., & Thinnukool, O. (2022). Optimal Deep Convolution Neural Network for Cervical Cancer Diagnosis Model.
Computers, Materials \& Continua,
70(2), 3295--3309.
https://doi.org/10.32604/cmc. 2022.020713
[18] William, W., Ware, A., Basaza-Ejiri, A. H., & Obungoloch, J. (2019). A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images.
BioMedical Engineering OnLine,
18(1), 16.
https://doi.org/10.1186/s12938-019-0634-5
[19] Hosseini, R., Dehmeshki, J., Barman, S., Mazinani, M., & Qanadli, S. (2010, February 13-18).
Modeling uncertainty in classification design of a computer-aided detection system [Conference session]
. Medical Imaging 2010: Computer-Aided Diagnosis, San Diego, California, United States.
https://doi.org/10.1117/12.844178
[20] Goodfellow, I., Bengio, Y., & Courville, A. (2016).
Deep Learning. Massachusetts Institute of Technology Press.
https://books.google.com/books?id=-s2MEAAAQBAJ
[21] Amiri, M. (2021). Fast Texture Retrieval Using Walsh-Hadamard Transform.
Quarterly Scientific Journal of Technical and Vocational University,
18(3), 137-153.
https:// doi.org/10.48301/kssa.2021.262698.1326
[22] Barootkar, M. (2023). Detecting the Probability of Stroke through Blood Plasma Measurement and ECG Examination using Fuzzy Logic.
Quarterly Scientific Journal of Technical and Vocational University,
20(1), 321-339.
https://doi.org/10. 48301/kssa.2023.354479.2231
[23] Nguyen, L. D., Lin, D., Lin, Z., & Cao, J. (2018, May 27-30).
Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation [Conference session]
. 2018 Institute of Electrical and Electronics Engineers International Symposium on Circuits and Systems, Florence, Italy.
https://doi.org/10.1109/ISCAS.2018.8351550
[24] Plissiti, M. E., Dimitrakopoulos, P., Sfikas, G., Nikou, C., Krikoni, O., & Charchanti, A. (2018, October 7-10).
Sipakmed: A New Dataset for Feature and Image Based Classification of Normal and Pathological Cervical Cells in Pap Smear Images [Conference session]
. 2018 25th Institute of Electrical and Electronics Engineers International Conference on Image Processing, Athens, Greece.
https://doi.org/10.1 109/ICIP.2018.8451588
[25] Sarwar, A., Suri, J., Ali, M., & Sharma, V. (2016). Novel benchmark database of digitized and calibrated cervical cells for artificial intelligence based screening of cervical cancer.
Journal of Ambient Intelligence and Humanized Computing,
7(4), 593-606.
https://doi.org/10.1007/s12652-016-0353-8
[26] Sokouti, B., Haghipour, S., & Tabrizi, A. D. (2014). A framework for diagnosing cervical cancer disease based on feedforward MLP neural network and ThinPrep histopathological cell image features.
Neural Computing and Applications,
24(1), 221-232.
https://doi.org/10.1007/s00521-012-1220-y
[27] Win, K. P., Kitjaidure, Y., Hamamoto, K., & Myo Aung, T. (2020). Computer-Assisted Screening for Cervical Cancer Using Digital Image Processing of Pap Smear Images.
Applied Sciences,
10(5), 1800.
https://doi.org/10.3390/app10051800
[28] Zhao, M., Wu, A., Song, J., Sun, X., & Dong, N. (2016). Automatic screening of cervical cells using block image processing.
BioMedical Engineering OnLine,
15(1), 14.
https://doi.org/10.1186/s12938-016-0131-z
[29] Denoeux, T. (2000). A neural network classifier based on Dempster-Shafer theory.
Institute of Electrical and Electronics Engineers Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans,
30(2), 131-150.
https://doi.org/10.1109/ 3468.833094
[30] Shafer, G. (1976).
A Mathematical Theory of Evidence. Princeton University Press.
https://books.google.com/books?id=wug9DwAAQBAJ
[31] Tong, Z., Xu, P., & Denœux, T. (2019). ConvNet and Dempster-Shafer Theory for Object Recognition. In N. Ben Amor, B. Quost, & M. Theobald (Eds.),
Scalable Uncertainty Management (pp. 368-381). Springer International Publishing.
https:// doi.org/10.1007/978-3-030-35514-2_27
[32] Rahaman, M. 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.
Computers in Biology and Medicine,
136(1), 104649.
https://doi.org/10.1016/j.compbiomed.2021.104649
[33] Bhatt, A. R., Ganatra, A., & Kotecha, K. (2021). Cervical cancer detection in pap smear whole slide images using convNet with transfer learning and progressive resizing.
PeerJ Comput Science,
7(1), e348.
https://doi.org/10.7717/peerj-cs.348
[34] Basak, H., Kundu, R., Chakraborty, S., & Das, N. (2021). Cervical Cytology Classification Using PCA and GWO Enhanced Deep Features Selection.
Springer Nature Computer Science,
2(5), 369.
https://doi.org/10.1007/s42979-021-00741-2
[35] Khamparia, A., Gupta, D., Rodrigues, J. J. P. C., & de Albuquerque, V. H. C. (2021). DCAVN: Cervical cancer prediction and classification using deep convolutional and variational autoencoder network.
Multimedia Tools and Applications,
80(20), 30399-30415.
https://doi.org/10.1007/s11042-020-09607-w