The evolving role of deep learning in image-based gynecological cancer Diagnosis: A comprehensive review

Abstract

Cancer remains a formidable global health challenge, significantly contributing to mortality and morbidity, with statistics projecting one in five women will be affected. In recent years, the field of medicine has witnessed a rapid surge in Deep Learning (DL) techniques, particularly in diagnostic applications, offering transformative potential. This systematic review comprehensively explores how advanced DL can revolutionize the diagnosis and treatment of critical gynecological cancers: cervical, ovarian, and endometrial malignancies. It provides a thorough overview of gynecological cancer and highlights the crucial importance of publicly available benchmark datasets for robust research. We meticulously examine recent advancements in medical image analysis, evaluating DL algorithms' efficacy, advantages, and inherent limitations through comparative analyses. The review dissects diverse methodologies—from image acquisition and feature extraction to segmentation and classification—elucidating intricate technical aspects. Crucially, it addresses multifaceted clinical and technical challenges encountered in applying DL in gynecologic oncology, while outlining promising avenues for future research to enhance precision in detection, classification, and ultimately, improve patient outcomes.

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