Advancement in Deep Learning Methods for Diagnosis and Prognosis of Cervical Cancer

Author:

Yadav Pankaj1,Gupta Akshat2ORCID,Parveen Alisha3,Kumar Abhishek45

Affiliation:

1. Department of Bioscience and Bioengineering, Indian Institute of Technology, Jodhpur, 342037 India

2. Department of Biotechnology, Motilal Nehru National Institute of Technology, Allahabad, Prayagraj, 211004, India

3. Rudolf-Zenker, Institute of Experimental Surgery, Rostock University Medical Center, Rostock, Germany

4. Institute of Bioinformatics, International Technology Park, Bangalore, 560066, India

5. Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, 576104, India

Abstract

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.

Funder

Indian Institute of Technology

Department of Biotechnology, Ministry of Science and Technology, India

Publisher

Bentham Science Publishers Ltd.

Subject

Genetics (clinical),Genetics

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