Abstract:
Cervical cancer falls under the top most cancers found in women of developing countries since last many years.
Classification of cervical cancer through a traditional microscopic approach is a monotonous and prolonged task. Most of the
time hospital doctors cannot identify the cancer cells as sometimes the nucleus is difficult to see with naked eyes. Due to the
different perspectives of doctors, cancer stages are classified falsely which leads to low recovery and late medication. The use
of Image Processing and Machine Learning technologies can take off misclassification and inaccurate prediction. Although
many deep learning techniques are available for cervical cancer cell detection and classification, performance of such
techniques for prediction and classification with the real and sample dataset is the main challenge. In this paper, we did a
thorough state-of-the-art review with the available current literature. The objective of this paper is to bring forth in-depth
knowledge to novice researchers with the thorough understanding of the architecture of the computer assisted classification
process. The current literature is studied, analyzed, and discussed with their approaches, results, and methodologies.
