Title:Advancement in Deep Learning Methods for Diagnosis and Prognosis of
Cervical Cancer
Volume: 23
Issue: 4
Author(s): Akshat Gupta, Alisha Parveen, Abhishek Kumar and Pankaj Yadav*
Affiliation:
- Department of Bioscience and Bioengineering, Indian Institute of
Technology, Jodhpur, 342037 India
Keywords:
Deep learning, cervical cancer, diagnosis, neural networks, risk prediction, sensitive screening.
Abstract: Cervical cancer is the leading cause of death in women, mainly in developing countries, including
India. Recent advancements in technologies could allow for more rapid, cost-effective, and
sensitive screening and treatment measures for cervical cancer. To this end, deep learning-based methods
have received importance for classifying cervical cancer patients into different risk groups. Furthermore,
deep learning models are now available to study the progression and treatment of cancerous
cervical conditions. Undoubtedly, deep learning methods can enhance our knowledge toward a better
understanding of cervical cancer progression. However, it is essential to thoroughly validate the deep
learning-based models before they can be implicated in everyday clinical practice. This work reviews
recent development in deep learning approaches employed in cervical cancer diagnosis and prognosis.
Further, we provide an overview of recent methods and databases leveraging these new approaches for
cervical cancer risk prediction and patient outcomes. Finally, we conclude the state-of-the-art approaches
for future research opportunities in this domain.
