Medical image feature extraction and selection based on InceptionV3 and Gini Index for cervical cancer cells identification

Abstract

Cervical cancer is one of the leading causes of death among women. The adverse effects of this cancer can be minimized with early diagnosis and treatment. In recent years, several machine learning models have been proposed for cervical cancer detection and prediction. In this paper, we evaluate a new framework that integrates feature embedding based on Inception v3 to detect cervical cell cancer from medical images, and use the Gini index to select the most informative features. The classification was executed employing k-Nearest Neighbors, Decision Tree, AdaBoost, Random Forest, and Artificial Neural Network algorithms. The implemented classifiers showed good accuracy results based on 9 and 5 selected features. The Random Forest algorithm outperformed the existent state-of-the-art research by achieving the best accuracy with only 5 features. These result show the efficiency of our model for the computer-assisted diagnosis and prevention of cervical cancer and could help physicians make diagnostic-based decision via pap-smear images.