Classification of Cervical Cancer Using Deep Learning and Machine Learning Approach

Abstract Human and material resources are scarce in countries such as developing countries with a high rate of cervical cancer. In such an environment, the introduction of automatic diagnostic technology that can replace specialists is urgent. Finding best method of the known methods can accelerate the adoption of computer-aided diagnostic tools for cervical cancer. In this paper, we would like to investigate which method, machine learning or deep learning, has higher classification performance in diagnosing cervical cancer.Using 4,119 sheets, cervical cancer was classified to positive or negative class using Resnet-50 for deep learning, XGB, SVM and RF for mechine learning. In both experiments, square images which of vaginal wall regions are cut were used. In the machine learning, 10 major features were extracted from a total of 300 features.All tests were validated by 5-fold cross-validation, and receiver operating characteristics(ROC) analysis yielded the following AUC: Resnet-50 0.97(CI 95% 0.949-0.976), XGB 0.82(CI 95% 0.797-0.851), SVM 0.84(CI 95% 0.801-0.854), RF 0.79(CI 95% 0.804-0.856). Deep learning was 0.15 point higher (p < 0.05) than the average (0.82) of three machine learning methods.We propose an better algorithm among the previously known or newly proposed algorithms for diagnosis of cervical cancer using cervicography images.