Tan, Sher Lyn and Selvachandran, Ganeshsree and Ding, Weiping and Paramesran, Raveendran and Kotecha, Ketan (2024) Cervical cancer classification from pap smear images using deep convolutional neural network models. Interdisciplinary Sciences-Computational Life Sciences, 16 (1). pp. 16-38. ISSN 1913-2751, DOI https://doi.org/10.1007/s12539-023-00589-5.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Cervical cancer classification; Cervical cancer detection; Pap smear images; Convolutional neural network; Deep learning; Medical image processing |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering > Department of Electrical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 06 Aug 2024 08:05 |
| Last Modified: | 06 Aug 2024 08:05 |
| URI: | http://eprints.um.edu.my/id/eprint/45978 |
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