COMPUTED-ASSISTED DIAGNOSIS IN DIAGNOSTIC CERVICAL CANCER IMAGING : A TEN YEARS SYSTEMATIC REVIEW

Abstract

Background: Cervical cancer is one of the most common malignant tumors in the world, and it is the fourth leading cause of cancer in women. The morbidity and mortality of cervical cancer in the developing countries are distinctly higher than those in the developed countries. Computer-assisted diagnosis is key for scaling up cervical cancer screening, but current algorithms perform poorly on whole slide image analysis and generalization. The aim: This study aims to show about computed-assisted diagnosis in diagnostic cervical cancer imaging. Methods: By comparing itself to the standards set by the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020, this study was able to show that it met all of the requirements. So, the experts were able to make sure that the study was as up-to-date as it was possible to be. For this search approach, publications that came out between 2014 and 2024 were taken into account. Several different online reference sources, like Pubmed and SagePub, were used to do this. It was decided not to take into account review pieces, works that had already been published, or works that were only half done. Result: In the PubMed database, the results of our search brought up 153 articles, whereas the results of our search on SagePub brought up 180 articles. The results of the search conducted for the last year of 2014 yielded a total 53 articles for PubMed and 72 articles for SagePub. The result from title screening, a total 18 articles for PubMed and 27 articles for SagePub. In the end, we compiled a total of 10 papers. We included five research that met the criteria. Conclusion: Computed-assisted medical diagnosis can successfully complete a variety of medical tasks by efficiently exploring the essence of a large amount of clinical data. The colposcopy-guided cervical biopsy is essential for detecting CIN in cervical cancer screening, but there are difficulties with increasing sensitivity globally.