Cervical cancer classification from pap smear images using deep convolutional neural network models
Document Type
Article
Publication Date
3-1-2024
Abstract
As one of the most common female cancers, cervical cancer often develops years after a prolonged and reversible pre-cancerous stage. Traditional classification algorithms used for detection of cervical cancer often require cell segmentation and feature extraction techniques, while convolutional neural network (CNN) models demand a large dataset to mitigate over-fitting and poor generalization problems. To this end, this study aims to develop deep learning models for automated cervical cancer detection that do not rely on segmentation methods or custom features. Due to limited data availability, transfer learning was employed with pre-trained CNN models to directly operate on Pap smear images for a seven-class classification task. Thorough evaluation and comparison of 13 pre-trained deep CNN models were performed using the publicly available Herlev dataset and the Keras package in Google Collaboratory. In terms of accuracy and performance, DenseNet-201 is the best-performing model. The pre-trained CNN models studied in this paper produced good experimental results and required little computing time.
Keywords
Cervical cancer classification, Cervical cancer detection, Pap smear images, Convolutional neural network, Deep learning, Medical image processing
Divisions
sch_ecs
Publication Title
Interdisciplinary Sciences-Computational Life Sciences
Volume
16
Issue
1
Publisher
Springer Science and Business Media Deutschland GmbH
Publisher Location
TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY