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

Abstract

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 × 224 × 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).