# Cervical Cytology Classification Using PCA & GWO Enhanced Deep Features Selection

arXiv: https://arxiv.org/abs/2106.04919 · HTML: https://mlsanity.com/paper/2106.04919/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 49 references that are in the map.
- First posted on arXiv 9 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [DeepPap: Deep Convolutional Networks for Cervical Cell Classification](https://mlsanity.com/paper/1801.08616/) (2018)
- [Multi-scale Attention U-Net (MsAUNet): A Modified U-Net Architecture for Scene Segmentation](https://mlsanity.com/paper/2009.06911/) (2020)
- [DFENet: A Novel Dimension Fusion Edge Guided Network for Brain MRI Segmentation](https://mlsanity.com/paper/2105.07962/) (2021)
- [SynCGAN: Using learnable class specific priors to generate synthetic data for improving classifier performance on cytological images](https://mlsanity.com/paper/2003.05712/) (2020)

## Built on by (most cited first, 10 of 10)

- [Addressing Class Imbalance in Semi-supervised Image Segmentation: A Study on Cardiac MRI](https://mlsanity.com/paper/2209.00123/) (2022)
- [PND-Net: Plant Nutrition Deficiency and Disease Classification using Graph Convolutional Network](https://mlsanity.com/paper/2410.12742/) (2024)
- [Cervical Optical Coherence Tomography Image Classification Based on Contrastive Self-Supervised Texture Learning](https://mlsanity.com/paper/2108.05081/) (2021)
- [Geometry-aware Gaussian Prior and Axial Attention for Cervical Cytology Image Classification](https://mlsanity.com/paper/2607.10278/) (2026)
- [RSO: A Novel Reinforced Swarm Optimization Algorithm for Feature Selection](https://mlsanity.com/paper/2107.14199/) (2021)
- [Ensemble of CNN classifiers using Sugeno Fuzzy Integral Technique for Cervical Cytology Image Classification](https://mlsanity.com/paper/2108.09460/) (2021)
- [A Voting-Stacking Ensemble of Inception Networks for Cervical Cytology Classification](https://mlsanity.com/paper/2308.02781/) (2023)
- [Interpretable pap smear cell representation for cervical cancer screening](https://mlsanity.com/paper/2311.10269/) (2023)
- [Fuzzy Rank-based Late Fusion Technique for Cytology image Segmentation](https://mlsanity.com/paper/2403.10884/) (2024)
- [AI Assisted Cervical Cancer Screening for Cytology Samples in Developing Countries](https://mlsanity.com/paper/2504.20435/) (2025)

## Abstract (arXiv)

Cervical cancer is one of the most deadly and common diseases among women worldwide. It is completely curable if diagnosed in an early stage, but the tedious and costly detection procedure makes it unviable to conduct population-wise screening. Thus, to augment the effort of the clinicians, in this paper, we propose a fully automated framework that utilizes Deep Learning and feature selection using evolutionary optimization for cytology image classification. The proposed framework extracts Deep feature from several Convolution Neural Network models and uses a two-step feature reduction approach to ensure reduction in computation cost and faster convergence. The features extracted from the CNN models form a large feature space whose dimensionality is reduced using Principal Component Analysis while preserving 99% of the variance. A non-redundant, optimal feature subset is selected from this feature space using an evolutionary optimization algorithm, the Grey Wolf Optimizer, thus improving the classification performance. Finally, the selected feature subset is used to train an SVM classifier for generating the final predictions. The proposed framework is evaluated on three publicly available benchmark datasets: Mendeley Liquid Based Cytology (4-class) dataset, Herlev Pap Smear (7-class) dataset, and the SIPaKMeD Pap Smear (5-class) dataset achieving classification accuracies of 99.47%, 98.32% and 97.87% respectively, thus justifying the reliability of the approach. The relevant codes for the proposed approach can be found in: https://github.com/DVLP-CMATERJU/Two-Step-Feature-Enhancement