Segmentation in Cervical Cancer Detection: A Key Step in Early Diagnosis


  • Pothineni Syam Sowbhagya Sree, Kolli Hemanjali, Padilam Pranavi, J.S.S.N.S Aiswarya, Burra Vijaya Babu, Sandeep Kumar


Cervical cancer, Contouring, Papillomavirus, Segmentation, Three Segnet Architecture


Cervical cancer refers to a type of cancer that develops in the cells of the cervix, which's the lower part of the uterus connecting to the vagina. Many cancers affect people all over the world. One of them is cervical cancer. Preventing the disease requires early detection and successful treatment rather than recognizing the issue at an advanced stage.  These precautions can help prevent deadly cancer and contribute to a healthy life. This cancer can be treated well if it is detected early by a medical checkup for HPV lesions and risk factors for malignant cervix formation It is commonly triggered by the papillomavirus (HPV) a sexually transmitted infection. Globally cervical cancer ranks as the most prevalent cancer among women with around 570,000 new cases being diagnosed every year. Fortunately, this form of cancer is highly preventable through screenings and HPV vaccinations effectively reducing the risk of its development. Our research paper primarily focuses on enhancing cancer diagnosis and analysis by employing various techniques such as Contour segmentation, fitness score assessment, detection rate calculation, identification of optimal threshold values, geometric mean analysis, ROI examination, and three-segnet architecture. According to our research, we achieved a detection rate of 85%, a fitness score of 95%, a geometric mean of 90%, and positive results in the ROI examination. As a result of improving our techniques, we can provide better results for all images, resulting in better diagnosis and treatment. Continuing to innovate in medical imaging is crucial for providing the best possible care for cervical cancer patients.


Download data is not yet available.


Y.Wang, C.Song, M.Wang, Y.Xie, L.Mi; G.Wang, “Rapid, Label-Free, and Highly Sensitive Detection of Cervical Cancer With Fluorescence Lifetime Imaging Microscopy”, IEEE Journal of Selected Topics in Quantum Electronics ,Vol. 22, No.3, pp. 228-234.2016.

S. Kaaviya, V. Saranyadevi, M. Nirmala, “PAP smear image analysis for cervical cancer detection”, IEEE International conference on engineering and technology, pp. 1-4,2015.

P.Huang, S.Zhang, M.Li, J.Wang, C.Ma, B.Wang, X.Lv "Classification of Cervical Biopsy Images Based on LASSO and EL-SVM" , IEEE Access, Vol. 8,pp. 24219 - 24228, 2020.

P.Gupta, I.Jindal, A.Goyal, “Early Detection and Prevention of Cervical Cancer", IEEE 5th International Conference for Convergence in Technology ,Vol. 9, No.4, 2015.

K.M.A.Adweb, N.Cavus, B.Sekeroglu “Cervical Cancer Diagnosis Using Very Deep Networks Over Different Activation Functions”,Ieee Access ,Vol. 9, pp. 46612 - 46625, 2021.

W.Wu, H.Zhou, “Data-Driven Diagnosis of Cervical Cancer With Support Vector Machine-Based Approaches”, IEEE Access ,Vol. 5, pp. 25189 - 25195, 2017.

M.Xia, G.Zhang, C.Mu, B.Guan, M.Wang, “Cervical Cancer Cell Detection Based on Deep Convolutional Neura lNetwork”, 39th Chinese Control Conference, pp. 6527-6532, 2020.

B.Wang,Y.Zhang,C.Wu,F.Wang, “Multimodal MRI Analysis of Cervical Cancer on the Basis of Artificial Intelligence Algorithm”, Contrast Media & Molecular Imaging ,Vol. 2021, 2021.

Y.Ma,H.Zhu,Z.Yang,D.Wang, “Optimizing the Prognostic Model of Cervical Cancer Based on Artificial Intelligence Algorithm and Data Mining Technology”, Wireless Communications and Mobile Computing,Vol. 2022, pp.6, 2011.

K.Kawasaki.Y,Suehiro.T,Kunugi.K,Umayahara.T,Akiya.H,Iwabuchi,H.Sakunaga.M,Sakamoto.T, Sugishita, Y. Tenjin, “ Application of PDT for Uterine Cervical Cancer”, Diagnostic and Therapeutic Endoscopy, Vol. 5, pp. 183-190.

S.Fang,J.Yang,M.Wang,C.Liu,S.Liu,” An Improved Image Classification Method for Cervical Precancerous Lesions Based on ShuffleNet”, Computational Intelligence and Neuroscience, Vol. 2022, pp. 8, 2022.

Y.Li, J.Chen, P.Xue, C.Tang, J.Chang, C.Chu, K.Ma, Q.Li; Y.Zheng; Y.Qiao "Computer-Aided Cervical Cancer Diagnosis Using Time-Lapsed Colposcopic Images”, IEEE Transactions on Medical Imaging, Vol. 39, No.11, pp. 3403 - 3415, 2020.

S.Kumar Suman & Nishtha.H,” Predicting risk of Cervical Cancer: A case study of machine learning”, Journal of Statistics and Management Systems , Vol. 22 , No. 4, pp. 689–696,2019.

T.M.Alam, M.M.A.Khan, M.A.Iqbal, A.Wahab, M.Mushtaq "Cervical Cancer Prediction through Different Screening Methods using Data Mining" , International Journal of Advanced Computer Science and Applications, Vol. 10, No. 2, pp. 388-396, 2019.

M.Solar,J.P.P.Gonzalez "Computational Detection of Cervical Uterine Cancer", 2019 Sixth International Conference on eDemocracy & eGovernment, pp. 213-217, 2019.

T.Chen, W.Zheng, H.Ying, X.Tan, K.Li, X.Li, D.Z.Chen; J.Wu "A Task Decomposing and Cell Comparing Method for Cervical Lesion Cell Detection", IEEE Transactions on Medical Imaging , Vol. 41, No. 9, pp. 2432 - 2442,2022.

Y.Ming, X.Dong, J.Zhao, Z.Chen, H.Wang, N.Wu,” Deep learning-based multimodal image analysis for cervical cancer detection”, Methods, Vol. 205, pp. 46-52,2022.

Y.T. Kelman, H.L.Yitzhak, N.Shabairou, S.Finder, Z.Zalevsky, "Multi-Spectral Optimization for Tissue Probing Using Machine Learning", IEEE Photonics Journal, Vol. 13, No. 1, pp. 7800114, 2020.

H.Zhang, C.Chen, C. Ma, C.Chen, Z.Zhu; B.Yang, F.Chen, D.Jia, Y.Li, X.Lv "Feature Fusion Combined With Raman Spectroscopy for Early Diagnosis of Cervical Cancer", IEEE Photonics Journal, Vol. 13, No. 3, 2021.

S.Paul; Madhumita, “RFCM3: Computational Method for Identification of miRNA-mRNA Regulatory Modules in Cervical Cancer”, IEEE/ACM transactions on computational biology and bioinformatics , Vol.17, No. 5, pp. 1729 - 1740,2020.

S.Zhao , Yongjun.H , J.Qin , Z.Wang, “A Semisupervised Deep Learning Method for Cervical Cell Classification”, Analytical Cellular Pathology, Vol. 2022, pp. 12,2022.

H.Attique,S.Shah ,S.Jabeen, F.G.Khan, A.Khan,M.ELAffendi, “Multiclass Cancer Prediction Based on Copy Number Variation Using Deep Learning”, Computational Intelligence and Neuroscience, Vol. 2022, pp. 11,2022.

M.M.Carneiro, “ Cervical cancer elimination: rising up to the challenge”, Women & Health, Vol.61, No. 7, pp. 617– 618,2021.

X.Jia, X. Sun, X. Zhang, “Breast Cancer Identification Using Machine Learning”, Mathematical Problems in Engineering, Vol. 2022, pp. 8, 2022.

N.Anfinan, “Cervical Cancer Staging in Saudi Arabia Clinicoradiological Correlation”, BioMed Research International, Vol. 2019, pp. 4,2019.

J.Montenegro, O.Freitas-Silva ,A.J.Teodoro, “Molecular Mechanisms of Coffee on Prostate Cancer Prevention”, BioMed Research International, Vol.2022, pp. 12,2022.




How to Cite

Pothineni Syam Sowbhagya Sree. (2024). Segmentation in Cervical Cancer Detection: A Key Step in Early Diagnosis. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1957–1868. Retrieved from



Research Article