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Recent advancement in cervical cancer diagnosis for automated screening: a detailed review

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Abstract

Cervical cancer is one of the most common and dangerous diseases for women. Initial diagnosis and classification of cervical cancer are to reduce the mortality rate. The Pap smear images are widely employed  for the detection of cervical cancer in an automated manner; thereby, it enables reliable and accurate results. Recently, different kinds of soft computing techniques are used to deal with cervical cancer detection. In order to gain insight into recent advancements in the fields of study, this paper analyses most research papers between January 2010 and December 2020. This paper presents the graphical and organized view of the recent  research works. The study explored the scope for further research in soft computing methods for the segmentation and classification of cervical cancer. The review also carried out an analysis of cervical cancer detection by categorizing the referred papers into techniques focused on soft computing. This study will provide information for researchers, publishers, and experts to examine emerging research patterns in the field of cervical cancer detection from pap smear images.

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References

  • Acosta-Mesa HG, Cruz-Ramírez N, Hernández-Jiménez R (2009) Aceto-white temporal pattern classification using k-NN to identify precancerous cervical lesion in colposcopic images. Comput Biol Med 39(9):778–784

    Google Scholar 

  • Adem K, Kiliçarslan S, Cömert O (2019) Classification and diagnosis of cervical cancer with stacked autoencoder and softmax classification. Expert Syst Appl 115:557–564

    Google Scholar 

  • Aksoy A, Kale S (2010) Segmentation of cervical cell images, International Conference on Pattern Recognition, pp 2399–2402

  • Ali M, Abid S, Vinod S, and Jyotsna S (2017) Artificial neural network based screening of cervical cancer using a hierarchical modular neural network architecture (HMNNA) and novel benchmark uterine cervix cancer database, Neural Computing and Applications, pp 1–15

  • Amaro F, Menezes S, Nuovo GJ, Cunha CB, De Oliveira L, Pereira R, Oliveira-Silva M, Russomano F, Pires A, Nicol AF (2014) Correlation of MCM2 detection with stage and virology of cervical cancer. Int J Biol Markers 29(4):363–371

    Google Scholar 

  • Ariji Y, Yoshihiko S, Toru N, Atsushi N, Motoki F, Yoshitaka K, Michihito N, Masako N, Akitoshi K, Eiichiro A (2019) CT evaluation of extranodal extension of cervical lymph node metastases in patients with oral squamous cell carcinoma using deep learning classification. Oral Radiol 36:148–155

    Google Scholar 

  • Bergmeir S, Benítez, (2012) Segmentation of cervical cell nuclei in high-resolution microscopic images: A new algorithm and a web-based software framework. Comput Methods Programs Biomed 107(3):497–512

    Google Scholar 

  • De S, Joe Stanley R, Cheng Lu, Long R, Antani S, Thoma G, Zuna R (2013) A fusion-based approach for uterine cervical cancer histology image classification. Comput Med Imaging Graph 37(7–8):475–487

    Google Scholar 

  • Devi S, Panigrahi PK, Pradhan A (2014) Detecting cervical cancer progression through extracted intrinsic fluorescence and principal component analysis. J Biomed Opt 19(12):127003

    Google Scholar 

  • Devi A, Ravi V, Punitha S (2016) Classification of cervical cancer using artificial neural networks. Proced Comput Sci 89:465–472

    Google Scholar 

  • Geeitha S, Thangaman M (2018) Incorporating EBO-HSIC with SVM for gene selection associated with cervical cancer classification. J Med Syst 42(11):225

    Google Scholar 

  • Gertych A, Joseph A, Walts AE, Bose S (2012) Automated detection of dual p16/Ki67 nuclear immunoreactivity in liquid-based Pap tests for improved cervical cancer risk stratification. Ann Biomed Eng 40(5):1192–1204

    Google Scholar 

  • Goceri E, Goceri N (2017) Deep learning in medical image analysis: recent advances and future trends. In: International Conference Computer Graphics Vision Computer Vis.ion Image Processing. CGVCVIP 2017, Lisbon, Portugal, pp 305–310.

  • Greene MZ, Hughes TL, Hanlon A, Huang L, Sommers MS, Meghani SH (2019) Predicting cervical cancer screening among sexual minority women using classification and regression tree analysis. Prev Med Rep 13:153–159

    Google Scholar 

  • Guo P, Banerjee K, Stanley J, Long R, Antani S, Thoma G, Zuna R, Frazier SR, Moss RH, Stoecker WV (2015) Nuclei-based features for uterine cervical cancer histology image analysis with fusion-based classification. IEEE Journal Biomed Health Inform 20(6):1595–1607

    Google Scholar 

  • He H, Lin MB, Wang S, Li, (2012) Topographical distribution pattern of cervical intraepithelial neoplasia across the cervix. J Int Med Res 40(5):1897–1903

    Google Scholar 

  • Jemal A, Freddie B, Melissa M, Center JF, Elizabeth W, David F (2011) Global cancer statistics. Cancer J Clin 61(2):69

    Google Scholar 

  • Jusman Y, Ng SC, Abu NAO (2014) Intelligent screening systems for cervical cancer. Sci World J. https://doi.org/10.1155/2014/810368

    Article  Google Scholar 

  • Kashyap D, Abhishek S, Jatin S, Anupama B, Malay KD, Radim B, Kamil R (2016) Cervical cancer detection and classification using Independent Level sets and multi SVMs, International conference on telecommunications and signal processing (TSP), pp 523–528

  • Kim K-B, Sungshin K, Kwee-Bo S (2010) Nucleus classification and recognition of uterine cervical pap-smears using fuzzy ART algorithm. In: Asia-Pacific Conference on Simulated Evolution and Learning, pp 560–567

  • Kong H, Gurcan M, Belkacem-Boussaid K (2011) Partitioning histopathological images: an integrated framework for supervised color-texture segmentation and cell splitting. IEEE Trans Med Imaging 30(9):1661–1677

    Google Scholar 

  • Kudva V, Keerthana P, Shyamala G (2019) Hybrid transfer learning for classification of uterine cervix images for cervical cancer screening. J Dig Imaging 33(3):619–631. https://doi.org/10.1007/s10278-019-00269-1

    Article  Google Scholar 

  • Kuko M, Mohammad P (2019) An ensemble machine learning method for single and clustered cervical cell classification. In: 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI), pp 216–222

  • Li C, JinxiuKe JL, Jingjing Su (2020) DNA methylation data–based molecular subtype classification related to the prognosis of patients with cervical cancer. J Cell Biochem 121(3):2713–2724

    Google Scholar 

  • Liu J, Peng Y, Zhang Y (2019) A Fuzzy reasoning model for cervical intraepithelial neoplasia classification using temporal grayscale change and textures of cervical images during acetic acid tests. IEEE Access 7:13536–13545

    Google Scholar 

  • Liu L, Xiao W, Miao Z, Sun and Zhang, (2020) Segmentation of organs-at-risk in cervical cancer CT images with a convolutional neural network. Physica Med 69:184–191

    Google Scholar 

  • Lu J, Song E, Ghoneim A, Alrashoud M (2020) Machine learning for assisting cervical cancer diagnosis: an ensemble approach. Future Gener Comput Syst 106:199–205

    Google Scholar 

  • Mabeya H, Khozaim K, Liu T, Orango O, Chumba D, Pisharodi L, Carter J, Cu-Uvin S (2012) Comparison of conventional cervical cytology versus visual inspection with acetic acid among human immunodeficiency virus-infected women in Western Kenya. J Lower Genit Tract Dis 16(2):92–97

    Google Scholar 

  • Marinakis Y, Marinaki M, Dounias G (2008) Particle swarm optimization for pap-smear diagnosis. Expert Syst Appl 35(4):1645–1656

    Google Scholar 

  • Moshavegh R, Babak EB, Andrew M, Sujathan K, Patrik M, Ewert B (2012) Automated segmentation of free-lying cell nuclei in pap smears for malignancy-associated change analysis. International Conference of the IEEE Engineering in Medicine and Biology Society, pp 5372–5375

  • Nithin S, Sharma P, Vivek M (2015) Automated cervical cancer detection using photonic crystal based bio-sensor. IEEE International Advance Computing Conference (IACC), pp 1174–1178

  • Erkaymaz Okan, Tuğba P (2018) Classification of cervical cancer data and the effect of random subspace algorithms on classification performance. In: 2018 26th Signal Processing and Communications Applications Conference (SIU), pp 1–4

  • Pai P-Y, Chang C-C, Chan Y-K (2012) Nucleus and cytoplast contour detector from a cervical smear image. Expert Syst Appl 39(1):154–161

    Google Scholar 

  • Park KJ (2020) Kay Cervical adenocarcinoma: integration of HPV status, pattern of invasion, morphology and molecular markers into classification. Histopathology 76(1):112–127

    Google Scholar 

  • Plissiti M, Nikou C, Charchanti A (2010) Watershed-based segmentation of cell nuclei boundaries in Pap smear images. IEEE International Conference on Information Technology and Applications in Biomedicine, pp 1–4

  • Pu Y, Jagtap J, Pradhan A, Alfano RR (2014) Spatial frequency analysis for detecting early stage of cancer in human cervical tissues. Technol Cancer Res Treat 13(5):421–425

    Google Scholar 

  • Qiu X, Tao N, Tan Y, Xinxing Wu (2010) Constructing of the risk classification model of cervical cancer by artificial neural network. Expert Syst Appl 32(4):1094–1099

    Google Scholar 

  • Ramasamy R, Chinnasamy C (2019) Detection and segmentation of cancer regions in cervical images using fuzzy logic and adaptive neuro fuzzy inference system classification method. Int J Imaging Syst Technol 30:412–420

    Google Scholar 

  • Rejeesh MR (2019) Interest point based face recognition using adaptive neuro fuzzy inference system. Multimedia Tools and Applications 78(16):22691–22710

    Google Scholar 

  • Rejeesh MR, Thejaswini P (2020) MOTF: Multi-objective Optimal Trilateral Filtering based partial moving frame algorithm for image denoising. Multimedia Tools and Applications 79(37–38):28411–28430

    Google Scholar 

  • Richardson LA, Tota J, Franco EL (2011) Optimizing technology for cervical cancer screening in high-resource settings. Expert Rev Obstet Gynecol 6(3):343–353

    Google Scholar 

  • Roerdink JB, Meijster A (2000) The watershed transform: Definitions, algorithms and parallelization strategies. Fundam Inform 41(1):187–228

    MathSciNet  MATH  Google Scholar 

  • Sanyal P, Prosenjit G, Sanghita B (2019) Performance characteristics of an artificial intelligence based on convolutional neural network for screening conventional Papanicolaou-stained cervical smears. Med J Armed Forces India 76:418–424

    Google Scholar 

  • Sarwar A, Sharma V, Gupta R (2015) Hybrid ensemble learning technique for screening of cervical cancer using Papanicolaou smear image analysis. Personal Med Univ 4:54–62

    Google Scholar 

  • Savitha B, Subashini P (2013) An adaptive threshold segmentation for detection of nuclei in cervical cells using wavelet shrinkage algorithms. In: Third International Conference on Computer Science, Engineering and Applications.

  • Schilling T, Miroslaw L, Glabaad S (2010) Towards rapid cervical cancer diagnosis: automated detection and classification of pathologic cells in phase-contrast images. Int J Gynaecol Cancer 17(1):118–126

    Google Scholar 

  • Shao J, Zhuo Z, Huiying L, Ying S, Zhihan Y, Xue W, Zujun H (2020) DCE-MRI pharmacokinetic parameter maps for cervical carcinoma prediction. Comput Biol Med 118:103634

    Google Scholar 

  • Shariff A, Kangas J, Coelho LP, Quinn S, Murphy RF (2010) Automated image analysis for high-content screening and analysis. J Biomol Screen 15(7):726–734

    Google Scholar 

  • Sharma M, Singh SK, Agrawal P, Madaan V (2016) Classification of clinical dataset of cervical cancer using KNN. Indian J Sci Technol 9(28):1–5

    Google Scholar 

  • Sulaiman SitiNoraini, Mat-Isa NA, Othman NH, Ahmad F (2015) Improvement of features extraction process and classification of cervical cancer for the neuralpap system. Proced Comput Sci 60:750–759

    Google Scholar 

  • Sundararaj V (2019) Optimised denoising scheme via opposition-based self-adaptive learning PSO algorithm for wavelet-based ECG signal noise reduction. Int J Biomed Eng Technol 31(4):325

    Google Scholar 

  • Sundararaj V, Muthukumar S, Kumar RS (2018) An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks. Comput Sec 77:277–288

    Google Scholar 

  • Sundararaj V, Anoop V, Dixit P, Arjaria A, Chourasia U, Bhambri P, Rejeesh MR, Sundararaj R (2020) CCGPA-MPPT: cauchy preferential crossover-based global pollination algorithm for MPPT in photovoltaic system. Prog Photovolt 28(11):1128–1145

    Google Scholar 

  • Taha B, Jorge D, Naoufel W (2017) Classification of cervical-cancer using pap-smear images: a convolutional neural network approach. In: Annual Conference on Medical Image Understanding and Analysis, pp 261–272

  • Talukdar J, Nath CK, Talukdar PH (2013) Fuzzy clustering based image segmentation of pap smear images of cervical cancer cell using FCM algorithm. Markers 3(1):460

    Google Scholar 

  • Teeyapan K, Nipon T-U, Sansanee AW (2015) Application of support vector based methods for cervical cancer cell classification, IEEE international conference on control system, computing and engineering (ICCSCE), pp 514–519

  • Tseng C-J, Chi-Jie Lu, Chang C-C, Chen G-D, Chalong C (2017) Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence. Artif Intell Med 78:47–54

    Google Scholar 

  • Verma GK, Lather JS, Kaushal A (2019) MatConvNet-Based Fast Method For Cervical Mr Images Classification. Computational intelligence: theories applications and future directions-volume II. Springer, Berlin, pp 669–679

    Google Scholar 

  • Vidhubala S, Niraimathi R, Ramaswamy N, Mahadevan S (2019) Call for systematic population-based cervical cancer screening: findings from community-based screening camps in Tamil Nadu, India. Asian Pac J Cancer Prev 20(12):3703–3710

    Google Scholar 

  • Vinu S (2016) An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm. Int J Intell Eng Syst 9(3):117–126

    Google Scholar 

  • Vinu S (2019) Optimal task assignment in mobile cloud computing by queue based ant-bee algorithm. Wirel Pers Commun 104(1):173–197

    Google Scholar 

  • Wang P, Lirui W, Yongming L, Qi S, Shanshan L, Xianling H (2019) Automatic cell nuclei segmentation and classification of cervical Pap smear images. Biomed Signal Process Control 48:93–103

    Google Scholar 

  • William W, Ware A, Basaza-Ejiri AH, Obungoloch J (2018) A review of image analysis and machine learning techniques for automated cervical cancer screening from pap-smear images. Comput Methods Programs Biomed 164:15–22

    Google Scholar 

  • William W, Ware A, Basaza-Ejiri AH, Obungoloch J (2019a) Cervical cancer classification from Pap-smears using an enhanced fuzzy C-means algorithm. Inform Med Unlocked 14:23–33

    Google Scholar 

  • William W, Ware A, HabinkaBasaza-Ejiri A, Obungoloch J (2019b) Cervical cancer classification from Pap-smears using an enhanced fuzzy C-means algorithm. Inform Med Unlocked 14:23–33

    Google Scholar 

  • William W, Ware A, Basaza-Ejiri AH, Obungoloch J (2019c) A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images. Biomed Eng Online 18(1):16

    Google Scholar 

  • Xia C, Yang F, He Z, Cai Y (2010) iTRAQ-based quantitative proteomic analysis of the inhibition of cervical cancer cell invasion and migration by metformin. Biomed Pharmacother 123:109762

    Google Scholar 

  • Xu T, Zhang H, Xin C, Kim E, Long R, Xue Z, Antani S, Huang X (2017) Multi-feature based benchmark for cervical dysplasia classification evaluation. Pattern Recogn 63:468–475

    Google Scholar 

  • Xue Z, Rodney L, Sameer A, George T (2010) Automatic extraction of mosaic patterns in uterine cervix images. IEEE International Symposium on Computer-Based Medical Systems, pp 273–278

  • Yi D, Linghua K, Yanli Z (2019) Contrast-enhancing snapshot narrow-band imaging method for real-time computer-aided cervical cancer screening. J Dig Imaging 33:211–220

    Google Scholar 

  • Yoon BJ, Vaidyanathan PP (2004) Wavelet-based denoising by customized thresholding. In: 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE.

  • Yu W, Ormerod JT, Stewart M (2020) Variational discriminant analysis with variable selection. Statist Comput. https://doi.org/10.1016/j.patcog.2016.09.027

    Article  MathSciNet  MATH  Google Scholar 

  • Zahras D, Zuherman R (2018) Cervical cancer risk classification based on deep convolutional neural network, International Conference on Applied Information Technology and Innovation (ICAITI), pp 149–153

  • Zhang XQ, Zhao S-G (2019) Cervical image classification based on image segmentation preprocessing and a CapsNet network model. Int J Imaging Syst Technol 29(1):19–28

    Google Scholar 

  • Zhang C, Wang C, Liu Li (2011) A practical segmentation method for automated screening of cervical cytology. In: 2011 International Conference on Intelligent Computation and Bio-Medical Instrumentation, pp 140–143

  • Zhang T, Luo Y-M, Li P, Liu P-Z, Yong-Zhao Du, Sun P, Hua B, Xue H (2020) Cervical precancerous lesions classification using pre-trained densely connected convolutional networks with colposcopy images. Biomed Signal Process Control 55:101566

    Google Scholar 

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Chitra, B., Kumar, S.S. Recent advancement in cervical cancer diagnosis for automated screening: a detailed review. J Ambient Intell Human Comput 13, 251–269 (2022). https://doi.org/10.1007/s12652-021-02899-2

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