Comprehensive Assessment of Fine-Grained Wound Images Using a Patch-Based CNN With Context-Preserving Attention
<italic>Goal:</italic> Chronic wounds affect 6.5 million Americans. Wound assessment via algorithmic analysis of smartphone images has emerged as a viable option for remote assessment. <italic>Methods:</italic> We comprehensively score wounds based on the clinically-validated...
Saved in:
| Main Authors: | Ziyang Liu, Emmanuel Agu, Peder Pedersen, Clifford Lindsay, Bengisu Tulu, Diane Strong |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
|
| Series: | IEEE Open Journal of Engineering in Medicine and Biology |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9464711/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Chronic Wound Image Augmentation and Assessment Using Semi-Supervised Progressive Multi-Granularity EfficientNet
by: Ziyang Liu, et al.
Published: (2024-01-01) -
Multimodal AI for Home Wound Patient Referral Decisions From Images With Specialist Annotations
by: Reza Saadati Fard, et al.
Published: (2025-01-01) -
Guided Conditional Diffusion Classifier (ConDiff) for Enhanced Prediction of Infection in Diabetic Foot Ulcers
by: Palawat Busaranuvong, et al.
Published: (2025-01-01) -
Easy-to-Apply Hydrogel Patch for Field Treatment and Monitoring of Equine Wounds
by: María Emilia Zambroni, et al.
Published: (2025-04-01) -
Advancements in smart wearable patch systems for enhanced wound healing
by: Prasanthi Samathoti, et al.
Published: (2025-04-01)