YOLO_CC: Deep Learning based Approach for Early Stage Detection of Cervical Cancer from Cervix Images Using YOLOv5s Model

Abstract

Cervical Cancer (CC) is the fourth major cancer, which is responsible for a large number of deaths among women. Early stage detection of the cancer is the most effective solutions for decreasing the mortality rate. The lack of awareness, and costly clinical diagnosis are the major barrier for women to participate in clinical screening test to detect CC at an early stage. To address the issue, the study aims to find a deep learning based intelligent system to detect CC in the early stage using real time images. To build the model, cervical cancer pap-smear test image datasets were gathered and these were labeled and preprocessed. Then the YOLOv5 model was employed on the labeled dataset to train the model. Three latest versions of YOLOv5 model were applied in this study to find the most efficient model for building the intelligent system to detect CC at an early stage. All of the applied models provided satisfactory performance. Among all the applied models, YOLOv5s outperformed with 0.8279 precision and 0.8265 recall value. The performance of the study indicates that the proposed model highly potential to diagnose CC using real time images in early stage. In the medical field, the proposed will be quite useful for clinicians, and medical professionals.(CC) is the fourth major cancer, which is responsible for a large number of deaths among women. Early stage detection of the cancer is the most effective solutions for decreasing the mortality rate. The lack of awareness, and costly clinical diagnosis are the major barrier for women to participate in clinical screening test to detect CC at an early stage. To address the issue, the study aims to find a deep learning based intelligent system to detect CC in the early stage using real time images. To build the model, cervical cancer pap-smear test image datasets were gathered and these were labeled and preprocessed. Then the YOLOv5 model was employed on the labeled dataset to train the model. Three latest versions of YOLOv5 model were applied in this study to find the most efficient model for building the intelligent system to detect CC at an early stage. All of the applied models provided satisfactory performance. Among all the applied models, YOLOv5s outperformed with 0.8279 precision and 0.8265 recall value. The performance of the study indicates that the proposed model highly potential to diagnose CC using real time images in early stage. In the medical field, the proposed will be quite useful for clinicians, and medical professionals.