Deep Learning Techniques for Cervical Cancer Diagnosis based on
  Pathology and Colposcopy Images
Hana Ahmadzadeh Sarhangi Dorsa Beigifard Elahe Farmani Hamidreza Bolhasani
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.