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."
