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: | Betelhem Zewdu Wubineh, Andrzej Rusiecki, Krzysztof Halawa |
|---|---|
| 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!
|
Similar Items
-
SE-DeepLabV3+: Cervical Cell Segmentation and Classification Using a Novel SE-Based DeepLabV3+ and Ensemble Method
by: Betelhem Zewdu Wubineh, et al.
Published: (2025-01-01) -
Cells Grouping Detection and Confusing Labels Correction on Cervical Pathology Images
by: Wenbo Pang, et al.
Published: (2024-12-01) -
Conditional Generative Adversarial Networks and Deep Learning Data Augmentation: A Multi-Perspective Data-Driven Survey Across Multiple Application Fields and Classification Architectures
by: Lucas C. Ribas, et al.
Published: (2025-02-01) -
Data Imputation Based on Retrieval-Augmented Generation
by: Xiaojun Shi, et al.
Published: (2025-06-01) -
Generative Artificial Intelligence for Synthetic Spectral Data Augmentation in Sensor-Based Plastic Recycling
by: Roman-David Kulko, et al.
Published: (2025-07-01)