  A comprehensive study on machine and deep learning techniques for
classification of normal and abnormal cervical cells by using pap smear images
from Herlev dataset results are presented. This dataset includes 917 images and
7 different classes. All techniques used in this study are modeled by using
Google Colab platform with scikit-learn and Keras library inside TensorFlow. In
the first study, traditional machine learning methods such as logistic
regression, k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Decision
Tree, Random Forest and eXtreme Gradient Boosting (XGBoost) are used and
compared with each other to find binary classification as normal and abnormal
cervical cells. Better results are observed by XGBoost and kNN classifiers
among the others with an accuracy of 85%. In the second study, a deep learning
model based on Convolutional Neural Network(CNN) is used for the same dataset.
Accordingly, accuracies of 99% and 93% are obtained for the training and the
test dataset, respectively. In this model, it takes 50 epochs to have these
accuracies within 20 minutes of computational time.
