Recent trends and analysis of Generative Adversarial Networks in Cervical Cancer Imaging

TL;DR

This paper reviews recent developments in Generative Adversarial Networks (GANs) for cervical cancer imaging, highlighting their applications, models, and evaluation metrics to improve early detection and diagnosis.

Contribution

It provides a comprehensive analysis of recent GAN-based methods in cervical cancer imaging, emphasizing their applications and performance evaluation.

Findings

GAN models are increasingly used in cervical cancer detection.

Various evaluation metrics are employed to assess GAN performance.

GAN-based techniques show promise in improving early diagnosis.

Abstract

Cervical cancer is one of the most common types of cancer found in females. It contributes to 6-29% of all cancers in women. It is caused by the Human Papilloma Virus (HPV). The 5-year survival chances of cervical cancer range from 17%-92% depending upon the stage at which it is detected. Early detection of this disease helps in better treatment and survival rate of the patient. Many deep learning algorithms are being used for the detection of cervical cancer these days. A special category of deep learning techniques known as Generative Adversarial Networks (GANs) are catching up with speed in the screening, detection, and classification of cervical cancer. In this work, we present a detailed analysis of the recent trends relating to the use of various GAN models, their applications, and the evaluation metrics used for their performance evaluation in the field of cervical cancer imaging.