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

被引:3
作者
Ontor, Md Zahid Hasan [1 ]
Ali, Md Mamun [1 ]
Ahmed, Kawsar [2 ,3 ]
Bui, Francis M. [3 ]
Al-Zahrani, Fahad Ahmed [4 ]
Mahmud, S. M. Hasan [5 ]
Azam, Sami [6 ]
机构
[1] Daffodil Int Univ DIU, Dept Software Engn SWE, Sukrabad 1207, Dhaka, Bangladesh
[2] Mawlana Bhashani Sci & Technol Univ, Dept Informat & Commun Technol, Grp Biophotomatix, Santosh 1902, Tangail, Bangladesh
[3] Univ Saskatchewan, Dept Elect & Comp Engn, 57 Campus Dr, Saskatoon, SK S7N 5A9, Canada
[4] Umm Al Qura Univ, Comp Engn Dept, Mecca 24381, Saudi Arabia
[5] Amer Int Univ Bangladesh AIUB, Dept Comp Sci, Kuratoli, Dhaka, Bangladesh
[6] Charles Darwin Univ, Coll Engn IT & Environm, Casuarina, NT 0909, Australia
来源
CMC-COMPUTERS MATERIALS & CONTINUA | 2023年 / 74卷 / 02期
基金
加拿大自然科学与工程研究理事会;
关键词
Cervical cancer; pap-smear; deep learning; cancerous cell; YOLOv5; model; EXPRESSION; PROLIFERATION; PROMOTES;
D O I
10.32604/cmc.2023.032794
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:3727 / 3741
页数:15
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