Meticulous review on Cutting-Edge Cervical Cancer cell Detection, Segmentation and Stratification of PapSmear Images using Image Processing and Machine learning Approach

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.