ABC-YOLO: Model Performance Comparison for Skin Burn Depth Classification

A comparative analysis of the proposed ABC-YOLO model against other state-of-the-art architectures for classifying skin burn depth. The dataset was created by Uğur Şevik and last updated on March 18, 2026. It likely contains statistical results from evaluating models on a source dataset of 358 retrospective burn images.

Use Cases

• Benchmarking object detection models based on reported evaluation metrics.
• Informing model selection for burn depth classification based on comparative performance.
• Analyzing the impact of data augmentation techniques on model performance for medical imaging.

Strengths

• Dataset is small (5.5 KB), enabling quick download and inspection.
• License is permissive (CC-BY-4.0), allowing for broad reuse and modification.
• Analysis is grounded in a specific medical imaging task with 358 source images.

Limitations

• Row count and specific column definitions are unknown, limiting suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.
• The dataset is very small (5.5 KB), indicating it contains summary statistics, not the raw image data.

Provenance

Source

Uğur Şevik via figshare.

Collection Method

Likely compiled from experimental results comparing the ABC-YOLO model to other studies.

Freshness

Last updated 2026-03-18 17:28:41; freshness should be verified.