license: mit
library_name: transformers
pipeline_tag: image-classification
base_model: facebook/convnextv2-tiny-22k-224
tags:
- chest-xray
- convnext
- convnextv2
- image-classification
- medical
- medical-imaging
- pneumonia
- pytorch
- transformers
datasets:
- keremberke/chest-xray-classification
metrics:
- accuracy
- f1
widget:
- src: >-
https://huggingface.co/kiselyovd/chest-xray-classifier/resolve/main/samples/bacterial_pneumonia.png
example_title: bacterial_pneumonia
- src: >-
https://huggingface.co/kiselyovd/chest-xray-classifier/resolve/main/samples/normal.png
example_title: normal
- src: >-
https://huggingface.co/kiselyovd/chest-xray-classifier/resolve/main/samples/viral_pneumonia.png
example_title: viral_pneumonia
model-index:
- name: kiselyovd/chest-xray-classifier
results:
- task:
type: image-classification
dataset:
type: keremberke/chest-xray-classification
name: Chest X-Ray Images (Pneumonia)
metrics:
- type: auroc_macro_ovr
value: 0.9752638346619307
- type: accuracy
value: 0.9134615384615384
- type: macro_f1
value: 0.9029730638714358
chest-xray-classifier
A 3-class chest X-ray classifier that distinguishes bacterial pneumonia, normal, and viral pneumonia on pediatric frontal chest radiographs. The main model is a ConvNeXt-V2-Tiny fine-tuned with the transformers library; a DINOv2 ViT-S linear probe serves as the baseline.
Medical disclaimer: this model is provided for research and educational purposes only. It is not a medical device and must not be used for clinical diagnosis or treatment decisions.
Metrics
Evaluated on the held-out test split (624 radiographs: 242 bacterial / 234 normal / 148 viral). All values are reported as percentages.
| Model | Accuracy | Macro F1 | Macro AUROC (OvR) |
|---|---|---|---|
| ConvNeXt-V2-Tiny (main) | 91.3% | 90.3% | 97.5% |
| DINOv2 ViT-S linear probe (baseline) | 85.6% | 84.2% | 94.2% |
Per-class results (main model)
| Class | Precision | Recall | F1 | Support |
|---|---|---|---|---|
| bacterial_pneumonia | 89.1% | 98.3% | 93.5% | 242 |
| normal | 99.5% | 89.7% | 94.4% | 234 |
| viral_pneumonia | 83.6% | 82.4% | 83.0% | 148 |
| Macro avg | 90.7% | 90.2% | 90.3% | 624 |
| Weighted avg | 91.7% | 91.3% | 91.3% | 624 |
Visualizations
All charts below are generated by scripts/make_plots.py, which runs this exact model over the full test split. The computed accuracy / macro-F1 / macro-AUROC are cross-checked against the committed evaluation report before any chart is rendered.
Confusion matrix
Counts and row-normalized rates on the 624-image test split. Bacterial pneumonia and normal are recognized strongly; the main source of error is viral pneumonia being predicted as bacterial pneumonia, which is the clinically hardest distinction in this dataset.
ROC curves (one-vs-rest)
One-vs-rest ROC curves with per-class AUROC. The normal class separates near-perfectly, while viral pneumonia is the most challenging, consistent with the confusion matrix.
Sample predictions
Representative correctly-classified test radiographs (two per class) with predicted class and softmax confidence.
Usage
import torch
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("kiselyovd/chest-xray-classifier")
model = AutoModelForImageClassification.from_pretrained("kiselyovd/chest-xray-classifier")
model.eval()
image = Image.open("your_radiograph.png").convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
probs = torch.softmax(logits, dim=-1)[0]
pred_id = int(probs.argmax())
print(model.config.id2label[pred_id], float(probs[pred_id]))
Class index order (model id2label): 0 -> bacterial_pneumonia, 1 -> normal, 2 -> viral_pneumonia.
Training data
Trained on the Chest X-Ray Images (Pneumonia) dataset of pediatric frontal chest radiographs. The original two-class layout (NORMAL / PNEUMONIA) is split into three classes by separating the PNEUMONIA images into bacterial and viral cases, producing the bacterial_pneumonia / normal / viral_pneumonia target space.
Intended use and limitations
Research and educational use only. This model is not a medical device, has not been clinically validated, and must not be used for diagnosis or any clinical decision-making. It was trained on a single pediatric dataset and may not generalize to adult radiographs, other imaging equipment, or different acquisition protocols. Users are responsible for evaluating fitness for their use case, including fairness, safety, and compliance with applicable regulations.
Source code
License
MIT.