A systematic review of effective data augmentation in cervical cancer detection
The rapid progress of AI has made computer-assisted systems essential in medical fields like cervical cytology analysis. Deep learning requires large datasets, but data scarcity and privacy concerns pose challenges. Data augmentation addresses this by generating additional images and improving model...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Polish Academy of Sciences
2025-06-01
|
| Series: | International Journal of Electronics and Telecommunications |
| Subjects: | |
| Online Access: | https://journals.pan.pl/Content/135235/5-4984-Wubineh-sk.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850132943412920320 |
|---|---|
| author | Betelhem Zewdu Wubineh Andrzej Rusiecki Krzysztof Halawa |
| author_facet | Betelhem Zewdu Wubineh Andrzej Rusiecki Krzysztof Halawa |
| author_sort | Betelhem Zewdu Wubineh |
| collection | DOAJ |
| description | The rapid progress of AI has made computer-assisted systems essential in medical fields like cervical cytology analysis. Deep learning requires large datasets, but data scarcity and privacy concerns pose challenges. Data augmentation addresses this by generating additional images and improving model accuracy and generalizability. This review examines effective augmentation techniques and top-performing deep-learning models for segmentation and classification in cervical cancer detection. Analyzing 57 articles, we found that hybrid deep feature fusion with augmentation (rotation, flipping, shifting, brightness adjustments) achieved 99.8% accuracy in binary and 99.1% in multiclass classification. Augmentation is vital for enhancing model performance in limited data scenarios. |
| format | Article |
| id | doaj-art-cda31d9961d84c63a5ec08a8d486e1be |
| institution | OA Journals |
| issn | 2081-8491 2300-1933 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | International Journal of Electronics and Telecommunications |
| spelling | doaj-art-cda31d9961d84c63a5ec08a8d486e1be2025-08-20T02:32:05ZengPolish Academy of SciencesInternational Journal of Electronics and Telecommunications2081-84912300-19332025-06-01vol. 71No 2369377https://doi.org/10.24425/ijet.2025.153582A systematic review of effective data augmentation in cervical cancer detectionBetelhem Zewdu Wubineh0https://orcid.org/0000-0002-4790-7449Andrzej Rusiecki1https://orcid.org/0000-0003-2239-1076Krzysztof Halawa2https://orcid.org/0000-0001-6508-0468Wroclaw University of Science and Technology, PolandWroclaw University of Science and Technology, PolandWroclaw University of Science and Technology, PolandThe rapid progress of AI has made computer-assisted systems essential in medical fields like cervical cytology analysis. Deep learning requires large datasets, but data scarcity and privacy concerns pose challenges. Data augmentation addresses this by generating additional images and improving model accuracy and generalizability. This review examines effective augmentation techniques and top-performing deep-learning models for segmentation and classification in cervical cancer detection. Analyzing 57 articles, we found that hybrid deep feature fusion with augmentation (rotation, flipping, shifting, brightness adjustments) achieved 99.8% accuracy in binary and 99.1% in multiclass classification. Augmentation is vital for enhancing model performance in limited data scenarios.https://journals.pan.pl/Content/135235/5-4984-Wubineh-sk.pdfcervical cancerdata augmentationdeep learningartificially generated images |
| spellingShingle | Betelhem Zewdu Wubineh Andrzej Rusiecki Krzysztof Halawa A systematic review of effective data augmentation in cervical cancer detection International Journal of Electronics and Telecommunications cervical cancer data augmentation deep learning artificially generated images |
| title | A systematic review of effective data augmentation in cervical cancer detection |
| title_full | A systematic review of effective data augmentation in cervical cancer detection |
| title_fullStr | A systematic review of effective data augmentation in cervical cancer detection |
| title_full_unstemmed | A systematic review of effective data augmentation in cervical cancer detection |
| title_short | A systematic review of effective data augmentation in cervical cancer detection |
| title_sort | systematic review of effective data augmentation in cervical cancer detection |
| topic | cervical cancer data augmentation deep learning artificially generated images |
| url | https://journals.pan.pl/Content/135235/5-4984-Wubineh-sk.pdf |
| work_keys_str_mv | AT betelhemzewduwubineh asystematicreviewofeffectivedataaugmentationincervicalcancerdetection AT andrzejrusiecki asystematicreviewofeffectivedataaugmentationincervicalcancerdetection AT krzysztofhalawa asystematicreviewofeffectivedataaugmentationincervicalcancerdetection AT betelhemzewduwubineh systematicreviewofeffectivedataaugmentationincervicalcancerdetection AT andrzejrusiecki systematicreviewofeffectivedataaugmentationincervicalcancerdetection AT krzysztofhalawa systematicreviewofeffectivedataaugmentationincervicalcancerdetection |