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RevelaCap/
clinical-skin-condition-v1

Name: clinical-skin-condition-v1
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Clinical Skin Condition Classifier — v1

Model ID: clinical_skin_condition_v1
Architecture: EfficientNet-B0 (PyTorch / torchvision)
Input type: Clinical / macroscopic skin photographs
Task: 5-class educational skin condition classification

⚠️ Important Disclaimers

This is an educational prototype only.
It is not a diagnostic device and has not been validated for clinical use.
It does not provide treatment advice.
Model confidence scores are not clinical certainty.
Performance varies across image quality, skin tone, lighting, and dataset source.
Clinical (macroscopic) images and dermoscopic images are different input modalities and must not be mixed. This model is trained on clinical photos only.
Do not use this model for patient triage, diagnosis, or treatment decisions.

Taxonomy — 5-Class Educational Labels

Class index	Label
0	Eczema / dermatitis
1	Urticaria / allergic reaction
2	Folliculitis / acne-like
3	Psoriasis / papulosquamous
4	Lesion — dermoscopic review recommended

Class 4 is a lesion-routing class — it signals that a dermoscopic review may be appropriate, not that a malignancy is present.

Input Requirements

Image type: Clinical / macroscopic skin photos (smartphone or camera photos of skin)
Input size: 224 × 224 px (resized internally)
Normalisation: ImageNet mean/std ([0.485, 0.456, 0.406] / [0.229, 0.224, 0.225])
Format: RGB, any standard image format (JPEG, PNG)
Do not use dermoscopic (dermatoscope-captured) images with this model

Training

Base weights: ImageNet-pretrained EfficientNet-B0 (torchvision)
Training datasets: Clinical V2 — a curated mix of public clinical skin image datasets
Optimiser: AdamW, lr=1e-4, weight_decay=0.01
Epochs: 5 (best checkpoint by validation macro-F1)
Class balancing: Weighted cross-entropy loss
Split: Fixed train / val / test — test set never seen during training

Performance (test set)

Metric	Value
Macro-F1	0.6527
Balanced accuracy	0.6700
Lesion-routing false negatives	70

Performance is reported on a held-out test set (n=1 515) combining google_scin and fitzpatrick17k sources.

Artifacts in This Repository

File	Description
`best_model.pth`	PyTorch state dict (EfficientNet-B0, 5-class head)
`class_to_idx.json`	Label → class index mapping
`training_history.csv`	Per-epoch train/val loss and macro-F1

Loading the Model

python

import torch
import json
from torchvision.models import efficientnet_b0
from huggingface_hub import hf_hub_download

# Download artifacts
ckpt_path = hf_hub_download("RevelaCap/clinical-skin-condition-v1", "best_model.pth")
idx_path  = hf_hub_download("RevelaCap/clinical-skin-condition-v1", "class_to_idx.json")

with open(idx_path) as f:
    class_to_idx = json.load(f)
idx_to_class = {v: k for k, v in class_to_idx.items()}

# Build model
model = efficientnet_b0(weights=None)
model.classifier[1] = torch.nn.Linear(model.classifier[1].in_features, 5)
model.load_state_dict(torch.load(ckpt_path, map_location="cpu"))
model.eval()

Known Limitations

Trained on public research datasets; real-world distribution shift may degrade performance.
SCIN subset (smartphone clinical photos) shows lower macro-F1 (0.43) than the fitzpatrick17k subset.
Skin tone representation in training data is uneven — performance may vary across Fitzpatrick skin types.
Model has not been evaluated on paediatric populations.
Not suitable for telemedicine, clinical decision support, or any patient-facing application without independent validation.

Citation / Project

Revela — Educational Skin Condition Prototype
Repository: github.com/romanpoluden/revela