Abstract
Cervical cancer continues to pose a significant global health challenge, highlighting the urgent need for accurate and efficient diagnostic techniques. Recent progress in deep learning has demonstrated considerable potential in improving the detection and classification of cervical cancer. This review presents a thorough analysis of deep learning methods utilized for cervical cancer diagnosis, with an emphasis on critical approaches, evaluation metrics, and the ongoing challenges faced in the field. We explore various deep learning architectures, particularly convolutional neural networks (CNNs), and their applications in the segmentation and classification of cervical cytology images. Key performance indicators, such as accuracy, sensitivity, specificity, and the area under the curve (AUC), are reviewed to assess the effectiveness of these models. Despite advancements, challenges like limited annotated datasets, inconsistencies in medical imaging, and the demand for more resilient models remain. Strategies like data augmentation, transfer learning, and semi-supervised learning are examined as potential solutions. This review synthesizes current research to guide future studies and clinical implementations, ultimately advancing early detection and treatment of cervical cancer through cutting-edge deep learning technologies.