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phat 23(1):

Research Article

DeepCerviCancer - Deep Learning-Based Cervical Image Classification using Colposcopy and Cytology Images

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  • @ARTICLE{10.4108/eetpht.9.3473,
        author={Madhura Kalbhor and Swati Shinde and Sagar Lahade and Tanupriya Choudhury},
        title={DeepCerviCancer - Deep Learning-Based Cervical Image Classification using Colposcopy and Cytology Images},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={9},
        number={1},
        publisher={EAI},
        journal_a={PHAT},
        year={2023},
        month={10},
        keywords={computer vision, smart Healthcare, Artificial Intelligence, Internet of Things, MoveNet, Pose estimation, Machine Learning, deep learning, KNN, SVM, LDA},
        doi={10.4108/eetpht.9.3473}
    }
    
  • Madhura Kalbhor
    Swati Shinde
    Sagar Lahade
    Tanupriya Choudhury
    Year: 2023
    DeepCerviCancer - Deep Learning-Based Cervical Image Classification using Colposcopy and Cytology Images
    PHAT
    EAI
    DOI: 10.4108/eetpht.9.3473
Madhura Kalbhor1, Swati Shinde2,*, Sagar Lahade1, Tanupriya Choudhury3
  • 1: Pimpri Chinchwad College of Engineering
  • 2: Pimpri Chinchwad College Of Engineering
  • 3: University of Petroleum and Energy Studies
*Contact email: swati.shinde@pccoepune.org

Abstract

INTRODUCTION:  Cervical cancer is a deadly malignancy in the cervix, affecting billions of women annually. OBJECTIVES: To develop deep learning-based system for effective cervical cancer detection by combining colposcopy and cytology screening. METHODS: It employs DeepColpo for colposcopy and DeepCyto+ for cytology images. The models are trained on multiple datasets, including the self-collected cervical cancer dataset named Malhari, IARC Visual Inspection with Acetic Acid (VIA) Image Bank, IARC Colposcopy Image Bank, and Liquid-based Cytology Pap smear dataset. The ensemble model combines DeepColpo and DeepCyto+, using machine learning algorithms.  RESULTS: The ensemble model achieves perfect recall, accuracy, F1 score, and precision on colposcopy and cytology images from the same patients.  CONCLUSION: By combining modalities for cervical cancer screening and conducting tests on colposcopy and cytology images from the same patients, the novel approach achieved flawless results.

Keywords
computer vision, smart Healthcare, Artificial Intelligence, Internet of Things, MoveNet, Pose estimation, Machine Learning, deep learning, KNN, SVM, LDA
Received
2023-06-20
Accepted
2023-09-24
Published
2023-10-03
Publisher
EAI
http://dx.doi.org/10.4108/eetpht.9.3473

Copyright © 2023 M. Kalbhor et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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