Deep Learning Techniques for Cervical Cancer Diagnosis based on Pathology and Colposcopy Images
This addresses the problem of human error in cervical cancer screening for women, but it is incremental as it reviews existing methods without introducing new ones.
This review article discusses how deep learning techniques can improve the accuracy and efficiency of cervical cancer diagnosis using pathology and colposcopy images, highlighting their potential to enhance precision and speed for early detection.
Cervical cancer is a prevalent disease affecting millions of women worldwide every year. It requires significant attention, as early detection during the precancerous stage provides an opportunity for a cure. The screening and diagnosis of cervical cancer rely on cytology and colposcopy methods. Deep learning, a promising technology in computer vision, has emerged as a potential solution to improve the accuracy and efficiency of cervical cancer screening compared to traditional clinical inspection methods that are prone to human error. This review article discusses cervical cancer and its screening processes, followed by the Deep Learning training process and the classification, segmentation, and detection tasks for cervical cancer diagnosis. Additionally, we explored the most common public datasets used in both cytology and colposcopy and highlighted the popular and most utilized architectures that researchers have applied to both cytology and colposcopy. We reviewed 24 selected practical papers in this study and summarized them. This article highlights the remarkable efficiency in enhancing the precision and speed of cervical cancer analysis by Deep Learning, bringing us closer to early diagnosis and saving lives.