Systematic Review of Deep Learning Techniques for Gynecological Cancer Diagnosis

Cancer remains a major global health concern, with estimates suggesting that nearly one in five women will be diagnosed with the disease during their lifetime. In recent years, deep learning (DL) has emerged as a transformative tool in medical diagnostics, showing particular promise in the detection and management of gynecological cancers, including cervical, ovarian, and endometrial types. This review critically examines the application of advanced DL techniques in gynecologic oncology, offering a detailed overview of the disease landscape and highlighting the vital role of open-access benchmark datasets in driving research progress. It explores the latest developments in DL-based medical image analysis, evaluating the performance, advantages, and limitations of various models. The discussion encompasses key stages such as image acquisition, feature extraction, segmentation, selection, and classification, shedding light on the technical complexities of DL workflows. Additionally, the review addresses clinical and computational challenges faced in real-world implementation and identifies future directions to enhance the accuracy and clinical utility of DL in gynecological cancer care.