Transformative Advances in Cervical Cancer Diagnosis Leveraging Colposcopy Imaging with ViT

This project addresses the urgent need for early and precise detection of cervical cancer types and stages, leveraging Vision Transformer models in deep learning. Key objectives include model development, user interface creation, ethical compliance, and real-world validation. By integrating various imaging tests, it offers a comprehensive approach to detection. Automation enhances efficiency, prioritizing ethical considerations like patient privacy. Validation in clinical settings ensures safety and efficacy, contributing significantly to healthcare advancements. Its scalable framework and research contributions underscore its broad impact on cervical cancer detection. Ultimately, it aims to enhance diagnostic accuracy and patient outcomes, advancing AI in healthcare.