"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."