Skip to main content

Advertisement

SpringerLink
Log in

Classification of normal and abnormal overlapped squamous cells in pap smear image

  • Original Article
  • Published:
International Journal of System Assurance Engineering and Management Aims and scope Submit manuscript

Abstract

Cervical cancer is considered the second major cancer and a deadly disease found in the uterine cervix of sexually active women which is possible to treat if found at an early stage using a pre-screening procedure. One such procedure is Pap smear test which helps to find abnormal cervical cells which may lead to cancer. The manual analysis of these Pap smear cell samples is challenging and is even more complex when cells are overlapped which demands the need for an automated system to reduce the complexity. The software-based automated system can be implemented using image processing techniques which can be installed on any computer, making it easy to use, and low cost. The main objective of this paper is to classify the overlapped cells which are pre-segmented using Mid-point segmentation algorithm. The Convolutional Neural Network (CNN) model with Rectified Linear Unit (ReLU) classifier classifies the given input images into two classes - normal and abnormal. The paper concentrates on the performance comparison between proposed method to other works and also with the manual prediction done by a cytotechnician. The model uses 917 pap smear cell images from Herlev Dataset for training and testing. The proposed model is evaluated on performance measures like precision, recall, F-score, support. The results reflect that the proposed model is best suited for Pap smear test analysis with 96% accuracy. Hence, proposed work makes a useful assistive tool for radiologists and clinicians to detect cervical cell abnormalities from pap smear cytology images.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Srivastava AN, Misra JS, Srivastava S, Das BC, Gupta S (2018) Cervical cancer screening in rural India: Status & current concepts. Indian J Med Res 148:687–696

    Article  Google Scholar 

  • https://doi.org/10.4103/ijmr.IJMR_5_17

  • Ewert Bengtsson P Malm(2014) Screening for Cervical Cancer Using Automated Analysis of PAP-Smears,Hindawi Publishing Corporation, Computational and Mathematical Methods in Medicine, 12, Article ID 842037.

  • https://doi.org/10.1155/2014/842037

  • Chen Y-F, Huang P-C, Lin K-C, Lin H-H, Wang L-E, Cheng (2014) Chung-Chuan and Chen, Tsung-Po and Chan, Yung-Kuan and Chiang, John Semi-Automatic Segmentation and Classification of Pap Smear Cells. IEEE Journal of Biomedical and Health Informatics, 18(1), 94–108. https://doi.org/10.1109/JBHI.2013.2250984. PMID: 24403407

  • Deepa TP, Dr. Nagaraja Rao A (2022) Extraction and Separation of Overlapped Squamous Cell Cytoplasm with Disjoint Nuclei in Cervical Pap Smear Image, Journal of Mobile Multimedia, Enabling AI Technologies Towards Multimedia Data Analytics for Smart Healthcare. 18. https://doi.org/10.13052/jmm1550-4646.1833

  • Anousouya Devi M, Ravi S, Vaishnavi J, Punitha S (2016) Classification of Cervical Cancer using Artificial Neural Networks. Elsevier B.V. Twelfth International Multi-Conference on Information Processing, Procedia Computer Science, 89,465–472

  • Saslow D, Solomon D, Lawson HW, Killackey M, Kulasingam SL, Cain J, Garcia FA, Moriarty AT, Waxman AG, Wilbur DC, Wentzensen N, Downs LS Jr, Spitzer M, Moscicki AB, Franco EL, Stoler MH, Schiffman M, Castle PE, Myers ER, ACS-ASCCP-ASCP Cervical Cancer Guideline Committee (2012) American Cancer Society, American Society for Colposcopy and Cervical Pathology, and American Society for Clinical Pathology Screening Guidelines for the Prevention and Early Detection of Cervical Cancer. J Lower Genit Tract Dis 16(3):175–204. https://doi.org/10.1097/LGT.0b013e31824ca9d5

    Article  Google Scholar 

  • Fontham ETH, Wolf AMD, Church TR, Etzioni R, Flowers CR, Herzig A, Guerra CE, Oeffinger KC, Shih YT, Walter LC, Kim JJ, Andrews KS, DeSantis CE, Fedewa SA, Manassaram-Baptiste D, Saslow D, Wender RC, Smith RA (2020) Cervical Cancer Screening for Individuals at Average Risk: Guideline Update from the American Cancer Society. CA: A Cancer Journal for Clinicians, 70(5), 321–346

  • https://doi.org/10.3322/caac.21628

  • Hussain E, Mahanta LB, Das CR, Talukdar RK (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 Cell 65:101347

    Article  Google Scholar 

  • https://doi.org/10.1016/j.tice.2020.101347

  • Jantzen J, Norup J, Dounias G, Bjerregaard B (2005) Pap-smear Benchmark Data for Pattern Classification. In Proc. NiSIS 2005: Nature inspired Smart Information Systems (NiSIS), EU co-ordination action, 1–9. NiSIS

  • http://www.nisis.de

  • DTU/Herlev Pap Smear Databases (2011) Retrieved August 2021 http://mde-lab.aegean.gr/index.php/downloads

  • Lin H, Hu Y, Chen S, Yao J, Zhang L(2018) Fine-Grained Classification of Cervical Cells Using Morphological and Appearance Based Convolutional Neural Networks. arXiv, 1–7

  • https://doi.org/10.48550/arXiv.1810.06058

  • Wu M, Yan C, Liu H, Liu Q, Yin Y(2018) Automatic classification of cervical cancer from cytological images by using convolutional neural network. Biosci Rep, 28;38(6): BSR20181769. Erratum in: Biosci Rep. 2019 Apr 2;39(4): PMID: 30341239; PMCID: PMC6259017

  • https://doi.org/10.1042/BSR20181769

  • Sompawong N, Mopan J, Pooprasert P, Himakhun W, Suwannarurk K, Ngamvirojcharoen J, Vachiramon T, Tantibundhit C(2019) Automated Pap Smear Cervical Cancer Screening Using Deep Learning. 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 7044–7048

  • https://doi.org/10.1109/EMBC.2019.8856369

  • Pallavi V, Mulmule, Rajendra D, Kanphade (2022) Classification of Cervical Cytology Overlapping Cell Images with Transfer Learning Architectures. Biomedical & Pharmacology Journal 15(1):277–284

    Article  Google Scholar 

  • https://doi.org/10.13005/bpj/2364

  • Nosrati MS, Hamarneh G(2015) Segmentation of overlapping cervical cells: A variational method with star-shape prior. IEEE 12th International Symposium on Biomedical Imaging (ISBI), New York, NY, 186–189

  • https://doi.org/10.1109/ISBI.2015.7163846

  • NVIDIA Corporation (2022) NVIDIA DIGITS - Interactive Deep Learning GPU Training System, Retrieved July 20 2022, from https://developer.nvidia.com/digits

  • Phoulady HA, Mouton, Peter R(2018) A New Cervical Cytology Dataset for Nucleus Detection and Image Classification (Cervix93) and Methods for Cervical Nucleus Detection. arXiv, 1811

  • https://doi.org/10.48550/arXiv.1811.09651

  • Su J, Xu X, He Y, Song J(2016) Automatic Detection of Cervical Cancer Cells by a Two-Level Cascade Classification System,Hindawi Publishing Corporation, Analytical Cellular Pathology, 11, Article ID 9535027.

  • https://doi.org/10.1155/2016/9535027

  • Yu S, Zhang S, Wang B, Dun H, Xu L, Huang X, Shi E, Feng X (2021) Generative adversarial network-based data augmentation to improve cervical cell classification model. Math Biosci engineering: MBE 18(2):1740–1752

    Article  Google Scholar 

  • https://doi.org/10.3934/mbe.2021090

  • Shanmugapriya P, Priscilla Vilma N Manorathi (2016) Feature Extraction and Classification for Segmentation of Overlapping Cervical Cells by Multiple Level Set Functions Optimization. Middle-East J Sci Res 24(S2):169–175

    Google Scholar 

  • https://doi.org/10.5829/idosi.mejsr.2016.24.S2.141

  • https://doi.org/10.30919/es8d633

  • Lidiya Thampi V Paul (2018) Automatic Segmentation and Classification in Cervical Cancer Images: Evaluation and Challenges. Int J Pure Appl Math 119(12):12549–12560

    Google Scholar 

  • The George Institute for Global health India (2018) Cervical Cancer in India: Challenge and Opportunities. Retrieved July 20 2022,https://www.georgeinstitute.org/cervical-cancer-in-india-challenges-andopportunities#:~:text=With%20agestandardized20incidence%20and,cervical%20cancer%20occur%20in%20India

  • Wasswa William A, Ware AH, Basaza-Ejiri J, Obungoloch (2019) Cervical cancer classification from Pap-smears using an enhanced fuzzy C-means algorithm. Inf Med Unlocked 14:23–33. https://doi.org/10.1016/j.imu.2019.02.001

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, VIT Deemed-to-be-University, 632014, Vellore, Tamil Nadu, India

    T. P. Deepa & A. Nagaraja Rao

Authors
  1. T. P. Deepa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Nagaraja Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Nagaraja Rao.

Ethics declarations

Disclosure of potential conflicts of interest

The authors have no competing interests to declare that are relevant to the content of this article.

Declarations

Not applicable.

Research involving Human Participants and/or Animals

This work does not involve Human Participants and Animals. This work uses images from DTU/Herlev Pap Smear Databases.

Informed consent

No funding was received to assist with the preparation of this manuscript.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deepa, T.P., Rao, A.N. Classification of normal and abnormal overlapped squamous cells in pap smear image. Int J Syst Assur Eng Manag 15, 519–531 (2024). https://doi.org/10.1007/s13198-022-01805-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-022-01805-z

Keywords

Search

Navigation