Early-Stage Cervical Cancerous Cell Detection from Cervix Images Using YOLOv5

Md Zahid Hasan Ontor, Md Mamun Ali, Kawsar Ahmed, Francis M. Bui, Fahad Ahmed Al-Zahrani, S. M. Hasan Mahmud, Sami Azam

    Research output: Contribution to journalArticlepeer-review

    4 Citations (Scopus)
    397 Downloads (Pure)

    Abstract

    Cervical Cancer (CC) is a rapidly growing disease among women throughout the world, especially in developed and developing countries. For this many women have died. Fortunately, it is curable if it can be diagnosed and detected at an early stage and taken proper treatment. But the high cost, awareness, highly equipped diagnosis environment, and availability of screening tests is a major barrier to participating in screening or clinical test diagnoses to detect CC at an early stage. To solve this issue, the study focuses on building a deep learning-based automated system to diagnose CC in the early stage using cervix cell images. The system is designed using the YOLOv5 (You Only Look Once Version 5) model, which is a deep learning method. To build the model, cervical cancer pap-smear test image datasets were collected from an open-source repository and these were labeled and preprocessed. Then the YOLOv5 models were applied to the labeled dataset to train the model. Four versions of the YOLOv5 model were applied in this study to find the best fit model for building the automated system to diagnose CC at an early stage. All of the model’s variations performed admirably. The model can effectively detect cervical cancerous cell, according to the findings of the experiments. In the medical field, our study will be quite useful. It can be a good option for radiologists and help them make the best selections possible.

    Original languageEnglish
    Pages (from-to)3727-3741
    Number of pages15
    JournalComputers, Materials and Continua
    Volume74
    Issue number2
    DOIs
    Publication statusPublished - 2023

    Bibliographical note

    Funding Information:
    Acknowledgement: The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: (22UQU4170008DSR07).

    Funding Information:
    Funding Statement: The project funding number is 22UQU4170008DSR07. This work was supported in part by funding from the Natural Sciences and Engineering Research Council of Canada (NSERC).

    Publisher Copyright:
    © 2023 Tech Science Press. All rights reserved.

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