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Cervical Cancer Diagnosis and Treatment with Artificial Intelligence and Early Cervical Malignant Growth Screening Detection Methodology | OMICS International| Abstract

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  • Current Trends Gynecol Oncol : 8 , Vol 8(2)

Cervical Cancer Diagnosis and Treatment with Artificial Intelligence and Early Cervical Malignant Growth Screening Detection Methodology

Qi Wan Yang*
School of Biosciences, The University of Porto is a Portuguese public research university located in Porto, Portugal
*Corresponding Author : Qi Wan Yang, School of Biosciences, The University of Porto is a Portuguese public research university located in Porto, Poland, Email: yang_wan@gmail.com

Received Date: Apr 01, 2023 / Published Date: Apr 28, 2023

Abstract

Artificial Intelligence in the Screening and Diagnosis of Cervical Cancer . The Abstract Cervical cancer is still the most common cancer that kills women, posing a serious threat to their mental and physical health. It is an effectively preventable disease with early screening and determination. Even though advances in technology have significantly improved the early detection of cervical cancer, accurate diagnosis is still challenging due to a number of factors. Applications of medical diagnostics based on artificial intelligence (AI) have increased in recent years and are highly applicable to the screening and diagnosis of cervical cancer. They save time, require less professional and technical staff, and are free of bias caused by subjective factors, among other advantages. We, hence, expected to examine how man-made intelligence can be utilized in cervical disease screening and finding, especially to work on the exactness of early conclusion.

Citation: Yang QW (2023) Cervical Cancer Diagnosis and Treatment with Artificial Intelligence and Early Cervical Malignant Growth Screening Detection Methodology. Current Trends Gynecol Oncol, 8: 144.

Copyright: © 2023 Yang QW. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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