Advancing Cervical Cancer Identification using Generative-based Adversarial Networks: An Integrative Learning Methodology

The Proposed system leveraged on Generative based Adversarial Networks (GANs) to enhance the detection and diagnosing of cervical based cancer by an integrative deep learning-based methodology. The system provides a comprehensive graphical representations of core cervical cancer-depended metrics, including age distribution, histological type distribution, tumour size distribution, and HPV based status distribution. By analyzing these parameters, the system aims to improve the accuracy level and efficacy of cervical cancer screening processes. The persons age distribution analysis helps to identify prevalent age groups affected by the disorder, while the histological type of distribution offers insights into the most common histological subtypes. Depending on tumour size, distribution will aid in understanding the range and severity of tumour sizes over the patients. Additionally, HPV status distribution highlighted the prevalence of HPV infection in cervical cancer cases. With advanced data visualization mechanisms, the proposed system facilitates a better understanding of the underlying data patterns and risk factors associated with cervical cancer, ultimately contributing to more informed clinically generated decision-making and to have a personalized patient care.