Recent developments in cervical cancer diagnosis using deep learning on whole slide images: An Overview of models,...

Integration of whole slide imaging (WSI) and deep learning technology has led to significant improvements in the screening and diagnosis of cervical cancer. WSI enables the examination of all cells on a slide simultaneously and deep learning algorithms can accurately label them as cancerous or non-cancerous. Although many studies have investigated the application of deep learning for diagnosing various diseases, there is a lack of research focusing on the evolution, limitations, and gaps of intelligent algorithms in conjunction with WSI for cervical cancer. This paper provides a comprehensive overview of the state-of-the-art deep learning algorithms used for the timely and precise analysis of cervical WSI images. A total of 115 relevant papers were reviewed, and 37 were selected after screening with specific inclusion and exclusion criteria. Methodological aspects including deep learning techniques, data sources, architectures, and classification techniques employed by the selected studies were analyzed. The review presents the most popular techniques and current trends in deep learning-based cervical classification systems, and categorizes the evolution of the domain based on deep learning techniques, citing an in-depth analysis of various models developed over time. The paper advocates for the implementation of transfer supervised learning when utilizing deep learning models such as ResNet, VGG19, and EfficientNet, and builds a solid foundation for applying relevant techniques in different fields. Although some progress has been made in developing novel models for the diagnosis of cervical cancer, substantial work remains to be done in creating standardized benchmark databases of WSI images for the research community. This paper serves as a comprehensive guide for understanding the fundamental concepts, benefits, and challenges related to various deep learning models on WSI, including their application for cervical system classification. Additionally, it provides valuable insights into future research directions in this area.