Digital twin application in women’s health: Cervical cancer diagnosis with CervixNet

Tools

Sharma, Vikas; Kumar, Akshi and Sharma, Kapil. 2024. Digital twin application in women’s health: Cervical cancer diagnosis with CervixNet. Cognitive Systems Research, 87, 101264. ISSN 2214-4366 [Article]

No full text available

Text
Updated Manuscript.pdf - Accepted Version
Permissions: Administrator Access Only until 10 January 2026.
Available under License Creative Commons Attribution Non-commercial No Derivatives.
Download (1MB)

Official URL: https://www.sciencedirect.com/science/article/abs/...

Abstract or Description

Digital Twin (DT) will transform digital healthcare and push it far beyond expectations. DT creates a virtual representation of a physical object reflecting its current state using real-time converted data. Nowadays, Women’s health is more frequently impacted by cervical cancer, but early detection and rapid treatment are critical factors in the cure of cervical cancer. This paper proposes and implements an automated cervical cancer detection DT framework in healthcare. This framework is a valuable approach to enhance digital healthcare operations. In this proposed work, the SIPaKMeD dataset was used for multi-cell classification. There were 1013 images (Input size 224 x 224x 3) in the collection, from which 4103 cells could be extracted. As a result, the CervixNet classifier model is developed using machine learning to detect cervical problems and diagnose cervical disease. Using pre-trained recurrent neural networks (RNNs), CervixNet extracted 1172 features, and after that, 792 features were selected using an independent principal component analysis (PCA) algorithm. The implemented models achieved the highest accuracy for predicting cervical cancer using different algorithms. The collected information has shown that integrating DT with the healthcare industry will enhance healthcare procedures by integrating patients and medical staff in a scalable, intelligent, and comprehensive health ecosystem. Finally, the suggested method produces an impressive 98.91% classification accuracy in all classes, especially for support vector machines (SVM).

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.1016/j.cogsys.2024.101264

Data Access Statement:

The dataset used in this study comes from the SIPaKMeD dataset, which contains 4103 manually separated pap smear cell images. This data set is publicly available online as of 2018. It is available on the corresponding website (accessed on 16 July 2022): https://www.kaggle.com/datasets/prahladmehandiratta/cervicalcancer-largest-dataset-sipakmed

Keywords:

Cervical Cancer, CervixNet, Digital twin (DT), Machine Learning (ML), Smart Healthcare

Departments, Centres and Research Units:

Computing

Dates: