Detection and prevention of cervical cancer using deep learning

Cervical cancer remains one of the leading causes of cancer-related deaths among women worldwide, particularly in low- and middle-income countries. Early detection significantly improves the chances of successful treatment and survival. This study explores advanced techniques for the early detection of cervical cancer using a combination of medical imaging, Pap smear analysis, and machine learning algorithms. Traditional diagnostic methods such as cytology (Pap smear) and HPV testing, though effective, are often time-consuming and require expert interpretation. In this work, a novel approach integrating automated image processing and classification models, such as convolutional neural networks (CNNs), is proposed to enhance diagnostic accuracy and reduce human error. The system is trained and validated using publicly available datasets, achieving high sensitivity and specificity in distinguishing between normal and abnormal cervical cells. The findings suggest that automated detection systems can play a critical role in large- scale screening programs, especially in resource- limited settings. Future research will focus on integrating multi-modal data and expanding the dataset to improve generalizability and robustness.