Open Access

ARTICLE

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

Md Zahid Hasan Ontor¹, Md Mamun Ali¹, Kawsar Ahmed^2,3,*, Francis M. Bui³, Fahad Ahmed Al-Zahrani⁴, S. M. Hasan Mahmud⁵, Sami Azam⁶

1 Department of Software Engineering (SWE), Daffodil International University (DIU), Sukrabad, Dhaka, 1207, Bangladesh
2 Group of Biophotomatiχ, Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, Bangladesh
3 Department of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, S7N5A9, SK, Canada
4 Computer Engineering Department, Umm Al-Qura University, Mecca, 24381, Saudi Arabia
5 Department of Computer Science, American International University-Bangladesh (AIUB), Kuratoli, Dhaka, 1229, Bangladesh
6 College of Engineering, IT and Environment, Charles Darwin University, Casuarina, 0909, NT, Australia

* Corresponding Authors: Kawsar Ahmed. Email: ,

Computers, Materials & Continua 2023, 74(2), 3727-3741. https://doi.org/10.32604/cmc.2023.032794

Received 30 May 2022; Accepted 26 August 2022; Issue published 31 October 2022

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.

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