Deep features selection through genetic algorithm for cervical pre-cancerous cell classification

Cervical cancer affects more than 500,000 women in the world annually accounting for about 6-9% of all cancer cases, but, its tedious detection procedure makes population-wide screening impossible. Classification of cervical pre-cancerous cells using computer-aided diagnosis tools is a challenging task and is posed an open problem for several decades. The most concerning issue is that only a small amount of data is available publicly. In this study, Deep Learning along with an evolutionary metaheuristic algorithm called the Genetic Algorithm is incorporated for cervical cell classification. Pre-trained Convolutional Neural Networks, namely GoogLeNet and ResNet-18 have been utilized to account for the fewer data available, for extracting deep features from the images. The extracted features are optimized by employing a Genetic Algorithm for feature selection which is coupled with the Support Vector Machines classifier for the final classification. The proposed method has been validated on two publicly available datasets which obtained promising results on 5-fold cross-validation justifying the framework to be reliable. The relevant source codes for the proposed framework has been provided in https://github.com/Rohit-Kundu/Cervical-Cancer-CNN-GAGitHub .