---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- nih-pc-chex-mimic_ch-google-openi-rsna
---
    
    
# densenet121-res224-all

A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs from all preceding layers and passes on its own feature-maps to all subsequent layers.
        
This model was trained on the datasets: nih-pc-chex-mimic_ch-google-openi-rsna and is described here: https://arxiv.org/abs/2002.02497

### How to use

Here is how to use this model to classify an image of xray:

```python
import urllib.request

import skimage
import torch
import torch.nn.functional as F
import torchvision
import torchvision.transforms

import torchxrayvision as xrv

model_name = "densenet121-res224-all"

img_url = "https://huggingface.co/spaces/torchxrayvision/torchxrayvision-classifier/resolve/main/16747_3_1.jpg"
img_path = "xray.jpg"
urllib.request.urlretrieve(img_url, img_path)

model = xrv.models.get_model(model_name, from_hf_hub=True)

img = skimage.io.imread(img_path)
img = xrv.datasets.normalize(img, 255)

# Check that images are 2D arrays
if len(img.shape) > 2:
    img = img[:, :, 0]
if len(img.shape) < 2:
    print("error, dimension lower than 2 for image")

# Add color channel
img = img[None, :, :]

transform = torchvision.transforms.Compose([xrv.datasets.XRayCenterCrop()])

img = transform(img)

with torch.no_grad():
    img = torch.from_numpy(img).unsqueeze(0)
    preds = model(img).cpu()
    output = {
        k: float(v)
        for k, v in zip(xrv.datasets.default_pathologies, preds[0].detach().numpy())
    }
print(output)

```
For more code examples, we refer to the [example scripts](https://github.com/kamalkraj/torchxrayvision/blob/master/scripts).


### Citation

Primary TorchXRayVision paper: [https://arxiv.org/abs/2111.00595](https://arxiv.org/abs/2111.00595)

```
Joseph Paul Cohen, Joseph D. Viviano, Paul Bertin, Paul Morrison, Parsa Torabian, Matteo Guarrera, Matthew P Lungren, Akshay Chaudhari, Rupert Brooks, Mohammad Hashir, Hadrien Bertrand
TorchXRayVision: A library of chest X-ray datasets and models. 
https://github.com/mlmed/torchxrayvision, 2020


@article{Cohen2020xrv,
author = {Cohen, Joseph Paul and Viviano, Joseph D. and Bertin, Paul and Morrison, Paul and Torabian, Parsa and Guarrera, Matteo and Lungren, Matthew P and Chaudhari, Akshay and Brooks, Rupert and Hashir, Mohammad and Bertrand, Hadrien},
journal = {https://github.com/mlmed/torchxrayvision},
title = {{TorchXRayVision: A library of chest X-ray datasets and models}},
url = {https://github.com/mlmed/torchxrayvision},
year = {2020}
arxivId = {2111.00595},
}


```
and this paper which initiated development of the library: [https://arxiv.org/abs/2002.02497](https://arxiv.org/abs/2002.02497)
```
Joseph Paul Cohen and Mohammad Hashir and Rupert Brooks and Hadrien Bertrand
On the limits of cross-domain generalization in automated X-ray prediction. 
Medical Imaging with Deep Learning 2020 (Online: https://arxiv.org/abs/2002.02497)

@inproceedings{cohen2020limits,
  title={On the limits of cross-domain generalization in automated X-ray prediction},
  author={Cohen, Joseph Paul and Hashir, Mohammad and Brooks, Rupert and Bertrand, Hadrien},
  booktitle={Medical Imaging with Deep Learning},
  year={2020},
  url={https://arxiv.org/abs/2002.02497}
}
```