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Deep Learning Approaches for Analysing Papsmear Images to Detect Cervical Cancer

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Abstract

The main reason for leading death in female is cervical cancer. The human Papillomavirus is responsible for all cervical cancer cases. There are several diagnostic procedures to find cervical cancer that includes Pap test, liquid-based cytology, colposcopy and HPV test. Early diagnosis is crucial for successful treatment and improved survival rates. In medical image analysis, methods of deep learning are showing promising results for the identification and grading of cervical carcinoma. The work uses dataset of cervical smear images and three cutting-edge deep learning models like ResNet50V2, InceptionV3, and Xception are applied and analysed for prediction of cervical cancer. The Models are verified using cross-validation and the performance are assessed using metrics like accuracy, precision, recall, and F1 score. According to the analysis, ResNet50V2 shows the highest accuracy. The obtained results imply that without the need for invasive procedures, deep learning techniques have the ability to accurately classify cervical cancer and greatly enhance early diagnosis.

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Data Availability

No associated data. The study is performed on open access database.

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Authors and Affiliations

  1. Department of Micro and Nanoelectronics, School of Electronics Engineering (SENSE), Vellore Institute of Technology, Vellore, India

    Somasundaram Devaraj

  2. Department of Biomedical Engineering, Dr N.G.P. Institute of Technology, Coimbatore, India

    Nirmala Madian

  3. Department of Computer Science and Engineering, School of Engineering, Malla Reddy University, Hyderabad, India

    M. Menagadevi

  4. Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, India

    R. Remya

Authors
  1. Somasundaram Devaraj
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  2. Nirmala Madian
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  3. M. Menagadevi
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  4. R. Remya
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Contributions

Somasundaram Devaraj—Experimentation and supervision, Nirmala Madian*—implementation of research, result analysis, manuscript drafting, M Menagadevi—manuscript drafting and database management, R Remya—database analysis and supervision.

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Correspondence to Nirmala Madian.

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Devaraj, S., Madian, N., Menagadevi, M. et al. Deep Learning Approaches for Analysing Papsmear Images to Detect Cervical Cancer. Wireless Pers Commun 135, 81–98 (2024). https://doi.org/10.1007/s11277-024-10986-8

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