Deep Learning Techniques–Based Medical Image Segmentation in Cervical Cancer
A. Saranya, S. Ravi, P. Harsha Latha, T. Kalaichelvi · 2025
Cervical cancer is the leading cause of cancer-related deaths among women worldwide, particularly in low- and middle-income nations with little access to screening programs; cervical cancer is the primary consideration of cancer-related deaths among women. Reduced mortality rates and improved patient outcomes are two benefits of early diagnosis. In detecting cervical cancer, deep learning helps to enhance the accuracy and reliability of diagnosis. However, there is still the possibility of advancement in these algorithms through more studies. One of the critical steps in computer-assisted cervical cancer detection is medical image segmentation. Convolutional neural networks have demonstrated considerable promise for accurate segmentation. This paper is a survey of medical image segmentation in cervical cancer detection using deep learning. It plays a significant role in image segmentation, carrying out the challenges in diagnosing cervical cancer. Various evaluation metrics used for cervical cancer detection and segmentation are also discussed. This survey highlights the progress and challenges in medical image segmentation and explains how deep learning is used to identify cervical cancer.