Purpose To compare deep learning with radiologists when diagnosing uterine cervical
cancer on a single T2-weighted image. Methods This study included 418 patients (age …

Cervical cancer is the second most common cancer in women worldwide with a mortality
rate of 60%. Cervical cancer begins with no overt signs and has a long latent period, making …

Cervical cancer is the most common cancer among women worldwide. The diagnosis and
classification of cancer are extremely important, as it influences the optimal treatment and …

Traditional screening of cervical cancer type classification majorly depends on the
pathologist's experience, which also has less accuracy. Colposcopy is a critical component …

Purpose Cervical cancer is the 4th most common cancer among women, worldwide.
Incidence and mortality rates are consistently increasing, especially in developing countries …

Accurate early detection of breast and cervical cancer is vital for treatment success. Here, we
conduct a meta-analysis to assess the diagnostic performance of deep learning (DL) …

This study aimed to propose a deep transfer learning framework for histopathological image
analysis by using convolutional neural networks (CNNs) with visualization schemes, and to …

To compare the diagnostic performance of deep learning models using convolutional neural
networks (CNN) with that of radiologists in diagnosing endometrial cancer and to verify …

Artificial intelligence has enabled the automated diagnosis of several cancer types. We
aimed to develop and validate deep learning models that automatically classify cervical …

INTRODUCTION: Cervical cancer is the leading cancer among the other female cancers. It
develops in the cervix of women. It takes decades in development thus can be preventable if …