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A comprehensive study on machine and deep learning techniques forclassification of normal and abnormal cervical cells by using pap smear imagesfrom Herlev dataset results are presented. This dataset includes 917 images and7 different classes. All techniques used in this study are modeled by usingGoogle Colab platform with scikit-learn and Keras library inside TensorFlow. Inthe first study, traditional machine learning methods such as logisticregression, k-Nearest Neighbors (kNN), Support Vector Machine (SVM), DecisionTree, Random Forest and eXtreme Gradient Boosting (XGBoost) are used andcompared with each other to find binary classification as normal and abnormalcervical cells. Better results are observed by XGBoost and kNN classifiersamong the others with an accuracy of 85%. In the second study, a deep learningmodel based on Convolutional Neural Network(CNN) is used for the same dataset.Accordingly, accuracies of 99% and 93% are obtained for the training and thetest dataset, respectively. In this model, it takes 50 epochs to have theseaccuracies within 20 minutes of computational time.

Comparison of Deep Learning and Traditional Machine Learning Techniques for Classification of Pap Smear Images