Comparison of deep transfer learning models for classification of cervical cancer from pap smear images
Abstract Cervical cancer is one of the most commonly diagnosed cancers worldwide, and it is particularly prevalent among women living in developing countries. Traditional classification algorithms often require segmentation and feature extraction techniques to detect cervical cancer. In contrast, co...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-74531-0 |
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| author | Harmanpreet Kaur Reecha Sharma Jagroop Kaur |
| author_facet | Harmanpreet Kaur Reecha Sharma Jagroop Kaur |
| author_sort | Harmanpreet Kaur |
| collection | DOAJ |
| description | Abstract Cervical cancer is one of the most commonly diagnosed cancers worldwide, and it is particularly prevalent among women living in developing countries. Traditional classification algorithms often require segmentation and feature extraction techniques to detect cervical cancer. In contrast, convolutional neural networks (CNN) models require large datasets to reduce overfitting and poor generalization. Based on limited datasets, transfer learning was applied directly to pap smear images to perform a classification task. A comprehensive comparison of 16 pre-trained models (VGG16, VGG19, ResNet50, ResNet50V2, ResNet101, ResNet101V2, ResNet152, ResNet152V2, DenseNet121, DenseNet169, DenseNet201, MobileNet, XceptionNet, InceptionV3, and InceptionResNetV2) were carried out for cervical cancer classification by relying on the Herlev dataset and Sipakmed dataset. A comparison of the results revealed that ResNet50 achieved 95% accuracy both for 2-class classification and for 7-class classification using the Herlev dataset. Based on the Sipakmed dataset, VGG16 obtained an accuracy of 99.95% for 2-class and 5-class classification, DenseNet121 achieved an accuracy of 97.65% for 3-class classification. Our findings indicate that DTL models are suitable for automating cervical cancer screening, providing more accurate and efficient results than manual screening. |
| format | Article |
| id | doaj-art-bab2792721a44a4297b7de5a6985a441 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-bab2792721a44a4297b7de5a6985a4412025-02-02T12:18:27ZengNature PortfolioScientific Reports2045-23222025-01-0115111810.1038/s41598-024-74531-0Comparison of deep transfer learning models for classification of cervical cancer from pap smear imagesHarmanpreet Kaur0Reecha Sharma1Jagroop Kaur2Department of Computer Science & Engineering, Punjabi UniversityDepartment of Electronics and Communication Engineering, Punjabi UniversityDepartment of Computer Science & Engineering, Punjabi UniversityAbstract Cervical cancer is one of the most commonly diagnosed cancers worldwide, and it is particularly prevalent among women living in developing countries. Traditional classification algorithms often require segmentation and feature extraction techniques to detect cervical cancer. In contrast, convolutional neural networks (CNN) models require large datasets to reduce overfitting and poor generalization. Based on limited datasets, transfer learning was applied directly to pap smear images to perform a classification task. A comprehensive comparison of 16 pre-trained models (VGG16, VGG19, ResNet50, ResNet50V2, ResNet101, ResNet101V2, ResNet152, ResNet152V2, DenseNet121, DenseNet169, DenseNet201, MobileNet, XceptionNet, InceptionV3, and InceptionResNetV2) were carried out for cervical cancer classification by relying on the Herlev dataset and Sipakmed dataset. A comparison of the results revealed that ResNet50 achieved 95% accuracy both for 2-class classification and for 7-class classification using the Herlev dataset. Based on the Sipakmed dataset, VGG16 obtained an accuracy of 99.95% for 2-class and 5-class classification, DenseNet121 achieved an accuracy of 97.65% for 3-class classification. Our findings indicate that DTL models are suitable for automating cervical cancer screening, providing more accurate and efficient results than manual screening.https://doi.org/10.1038/s41598-024-74531-0Deep learningCervical cancerPap smearTransfer learningImage classification |
| spellingShingle | Harmanpreet Kaur Reecha Sharma Jagroop Kaur Comparison of deep transfer learning models for classification of cervical cancer from pap smear images Scientific Reports Deep learning Cervical cancer Pap smear Transfer learning Image classification |
| title | Comparison of deep transfer learning models for classification of cervical cancer from pap smear images |
| title_full | Comparison of deep transfer learning models for classification of cervical cancer from pap smear images |
| title_fullStr | Comparison of deep transfer learning models for classification of cervical cancer from pap smear images |
| title_full_unstemmed | Comparison of deep transfer learning models for classification of cervical cancer from pap smear images |
| title_short | Comparison of deep transfer learning models for classification of cervical cancer from pap smear images |
| title_sort | comparison of deep transfer learning models for classification of cervical cancer from pap smear images |
| topic | Deep learning Cervical cancer Pap smear Transfer learning Image classification |
| url | https://doi.org/10.1038/s41598-024-74531-0 |
| work_keys_str_mv | AT harmanpreetkaur comparisonofdeeptransferlearningmodelsforclassificationofcervicalcancerfrompapsmearimages AT reechasharma comparisonofdeeptransferlearningmodelsforclassificationofcervicalcancerfrompapsmearimages AT jagroopkaur comparisonofdeeptransferlearningmodelsforclassificationofcervicalcancerfrompapsmearimages |