FOREX Press I. J. of Electrical & Electronics Research
Support Open Access
  • Home/
  • IJEER-110246

Research Article |

A Novel Approach to Cervical Cancer Detection Using Hybrid Stacked Ensemble Models and Feature Selection

Author(s): Pratiksha D.Nandanwar1* and Dr.Somnath B.Dhonde2

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 11, Issue 2 ,   Special Issue on Mobile Computing assisted by Artificial Intelligent for 5G/6G Radio Communication

Publisher : FOREX Publication

Published : 30 June 2023

e-ISSN : 2347-470X

Page(s) : 582-589

DOI: https://doi.org/10.37391/ijeer.110246

Abstract

Around the world, millions of women are diagnosed with cervical cancer each year. Early detection is very important to produce a better overall quality of life for those diagnosed with the disease and reduce the burden on the healthcare system. In recent years, the field of machine learning (ML) has been developing methods that can improve the accuracy of detecting cervical cancer. This paper presents a new approach to this problem by using a combination of image segmentation and feature extraction techniques. The proposed approach is divided into three phases. The first stage involves image segmentation, which is performed to extract the regions of interest from the input image. The second stage is comprised of extracting the features from the ROI with the help of the Histogram and Hu Moments techniques. The techniques used in this approach, namely the Hu Moments and Histogram techniques, respectively, can capture the shape information in the ROI. In the third stage of the project, we use a hybrid approach to classify the image. The proposed model is composed of several base classifiers, which are trained on varying subsets of the features that were extracted. These resulting classifiers then make a classification decision. We tested the proposed model against a large dataset of images for cervical cancer. The results of the experiments revealed that it performed better than the existing methods in detecting the disease. It was able to achieve an accuracy of 96.5%, an F1 score of 96.9%, and a recall of 96.7%. The proposed model was successful in accomplishing a remarkable accuracy of 96.5%, making it an ideal candidate for use in the detection of cervical cancer. It was also able to perform feature extraction using the Histogram techniques and image segmentation. The proposed method could help medical professionals improve the diagnosis and reduce the burden of this disease on women worldwide

Keywords: Cervical cancer, Cancer identification, Feature selection, Image segmentation Machine learning, Stack ensemble.




Pratiksha D.Nandanwar*, Department of Electronics and Telecommunication Engineering, PhD Research Scholar, AISSMS IOT, SavitribaiPhule University, Pune, India; Email: pratiskhadn@gmail.com

Dr. Dnyandeo Krishna Shedge, Department of Electronics and Telecommunication Engineering, Head of the Department & Professor, AISSMS College of Engineering, Pune, India; Email: entc.hod@aissmscoe.com

    [1] R. Elakkiya, V. Subramaniyaswamy, V. Vijayakumar, and A. Mahanti, “Cervical Cancer Diagnostics Healthcare System Using Hybrid Object Detection Adversarial Networks,” IEEE J. Biomed. Heal. Informatics, vol. 26, no. 4, pp. 1464–1471, 2022, doi: 10.1109/JBHI.2021.3094311. [Cross Ref]
    [2] W. Fan, Q. Lu, and G. Liu, “A Clinical Prediction Model of Overall Survival for Patients with Cervical Cancer Aged 25-69 Years,” Medicina (Kaunas)., vol. 59, no. 3, 2023, doi: 10.3390/medicina59030600. [Cross Ref]
    [3] S. Jahan et al., “Automated invasive cervical cancer disease detection at early stage through suitable ML model,” SN Appl. Sci., vol. 3, no. 10, 2021, doi: 10.1007/s42452-021-04786-z. [Cross Ref]
    [4] K. Kaushik et al., “A ML-Based Framework for the Prediction of Cervical Cancer Risk in Women,” Sustain., vol. 14, no. 19, 2022, doi: 10.3390/su141911947.
    [5] V. Kudva, K. Prasad, and S. Guruvare, “Transfer Learning for Classification of Uterine Cervix Images for Cervical Cancer Screening,” Lect. Notes Electr. Eng., vol. 614, pp. 299–312, 2020, doi: 10.1007/978-981-15-0626-0_25. [Cross Ref]
    [6] PNV Srinivasa Rao and PVY Jayasree (2023), Evaluation of IIOT based Pd-MaaS using CNN with Ensemble Subspace Discriminate – for Indian Ship Building in Maritime Industry. IJEER 11(1), 103-111. DOI: 10.37391/IJEER.110114. [Cross Ref]
    [7] M. Rahimi, A. Akbari, F. Asadi, and H. Emami, “Cervical cancer survival prediction by ML algorithms: a systematic review,” BMC Cancer, vol. 23, no. 1, p. 341, 2023, doi: 10.1186/s12885-023-10808-3. [Cross Ref]
    [8] S. Fekri-Ershad and M. F. Alsaffar, “Developing a Tuned Three-Layer Perceptron Fed with Trained Deep Convolutional Neural Networks for Cervical Cancer Diagnosis,” Diagnostics, vol. 13, no. 4, 2023, doi: 10.3390/diagnostics13040686 [Cross Ref]
    [9] L. Guo, W. Wang, X. Xie, S. Wang, and Y. Zhang, “ML for genetic prediction of chemotherapy toxicity in cervical cancer,” Biomed. Pharmacother., vol. 161, p. 114518, 2023, doi: 10.1016/j.biopha.2023.114518. [Cross Ref]
    [10] Q. M. Ilyas and M. Ahmad, “An Enhanced Ensemble Diagnosis of Cervical Cancer: A Pursuit of Machine Intelligence towards Sustainable Health,” IEEE Access, vol. 9, pp. 12374–12388, 2021, doi: 10.1109/ACCESS.2021.3049165. [Cross Ref]
    [11] Dr. P. Nancy, S Ravi Kishan, Kantilal Pitambar Rane, Dr. Karthikeyan Kaliyaperumal, Dr. Meenakshi and I Kadek Suartama (2022), Optimized Feature Selection and Image Processing Based Machine Learning Technique for Lung Cancer Detection. IJEER 10(4), 888-894. DOI: 10.37391/IJEER.100423. [Cross Ref]
    [12] Y. R. Park, Y. J. Kim, W. Ju, K. Nam, S. Kim, and K. G. Kim, “Comparison of machine and deep learning for the classification of cervical cancer based on cervicography images,” Sci. Rep., vol. 11, no. 1, pp. 1–11, 2021, doi: 10.1038/s41598-021-95748-3. [Cross Ref]
    [13] K. M. M. Uddin, N. Biswas, S. T. Rikta, and S. K. Dey, “ML-based diagnosis of breast cancer utilizing feature optimization technique,” Comput. Methods Programs Biomed. Updat., vol. 3, no. February, p. 100098, 2023, doi: 10.1016/j.cmpbup.2023.100098. [Cross Ref]
    [14] M. Sharma, “Cervical cancer prognosis using genetic algorithm and adaptive boosting approach,” Health Technol. (Berl)., vol. 9, no. 5, pp. 877–886, 2019, doi: 10.1007/s12553-019-00375-8. [Cross Ref]
    [15] A. A. Shah et al., “ML-driven identification of novel patient factors for prediction of major complications after posterior cervical spinal fusion,” Eur. Spine J., vol. 31, no. 8, pp. 1952–1959, 2022, doi: 10.1007/s00586-021-06961-7. [Cross Ref]
    [16] S. P. Deng, L. Zhu, and D. S. Huang, “Predicting Hub Genes Associated with Cervical Cancer through Gene Co-Expression Networks,” IEEE/ACM Trans. Comput. Biol. Bioinforma., vol. 13, no. 1, pp. 27–35, 2016, doi: 10.1109/TCBB.2015.2476790. [Cross Ref]
    [17] K. Fernandes, J. S. Cardoso, and J. Fernandes, “Automated Methods for the Decision Support of Cervical Cancer Screening Using Digital Colposcopies,” IEEE Access, vol. 6, pp. 33910–33927, 2018, doi: 10.1109/ACCESS.2018.2839338. [Cross Ref]
    [18] C. Li et al., “Cervical Histopathology Image Classification Using Multilayer Hidden Conditional Random Fields and Weakly Supervised Learning,” IEEE Access, vol. 7, pp. 90378–90397, 2019, doi: 10.1109/ACCESS.2019.2924467. [Cross Ref]

Pratiksha D. Nandanwar and Dr. Somnath B. Dhonde (2023), A Novel Approach to Cervical Cancer Detection Using Hybrid Stacked Ensemble Models and Feature Selection. ijeer 11(2), 582-589. DOI: 10.37391/ijeer.110246.