Chronic Wound Image Augmentation and Assessment Using Semi-Supervised Progressive Multi-Granularity EfficientNet
<italic>Goal:</italic> Augment a small, imbalanced, wound dataset by using semi-supervised learning with a secondary dataset. Then utilize the augmented wound dataset for deep learning-based wound assessment. <italic>Methods:</italic> The clinically-validated Photographic Wou...
Saved in:
| Main Authors: | Ziyang Liu, Emmanuel Agu, Peder Pedersen, Clifford Lindsay, Bengisu Tulu, Diane Strong |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Open Journal of Engineering in Medicine and Biology |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10050724/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Comprehensive Assessment of Fine-Grained Wound Images Using a Patch-Based CNN With Context-Preserving Attention
by: Ziyang Liu, et al.
Published: (2021-01-01) -
Cassava Diseases Classification using EfficientNet Model with Imbalance Data Handling
by: Stephany Octaviani Ngesthi, et al.
Published: (2024-08-01) -
Fault Diagnosis of Semi-Supervised Electromechanical Transmission Systems Under Imbalanced Unlabeled Sample Class Information Screening
by: Chaoge Wang, et al.
Published: (2025-02-01) -
Generative Adversarial learning with Negative Data Augmentation for Semi-supervised Text Classification
by: Shahriar Shayesteh, et al.
Published: (2022-05-01) -
DaNet: Domain-adaptive white blood cell classification through synthetic augmentation and cross-domain feature alignment
by: Wenpeng Gao, et al.
Published: (2025-07-01)