A simple rest api that classifies pneumonia infection weather it is Normal, Pneumonia Virus or Pneumonia Bacteria from a chest-x-ray image.

Related tags

Deep Learningpythonmachine-learningcomputer-visionpytorchartificial-intelligenceimage-classificationdeeplearning
Overview

Pneumonia Classification

This is a simple REST api that is served to classify pneumonia given an X-ray image of a chest of a human being. The following are expected results when the model does it's classification.

  1. pneumonia bacteria
  2. pneumonia virus
  3. normal

Starting the server

To run this server and make prediction on your own images follow the following steps

  1. create a virtual environment and activate it
  2. run the following command to install packages
pip install -r requirements.txt
  1. navigate to the app.py file and run
python app.py

Model

We are using a simple Multi Layer Perceptron (MLP) achitecture to do the categorical image classification on chest-x-ray images which looks simply as follows:

class MLP(nn.Module):
    def __init__(self, input_dim, output_dim, dropout=.5):
        super(MLP, self).__init__()
        self.input_fc = nn.Linear(input_dim, 250)
        self.hidden_fc = nn.Linear(250, 100)
        self.output_fc = nn.Linear(100, output_dim)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        batch_size = x.shape[0]
        x = x.view(batch_size, -1)
        x = F.relu(self.input_fc(x))
        x = self.dropout(x)
        x = F.relu(self.hidden_fc(x))
        x = self.dropout(x)
        outputs = self.output_fc(x)
        return outputs, x

All images are transformed to grayscale.

Model Metrics

The following table shows all the metrics summary we get after training the model for few 10 epochs.

model name model description test accuracy validation accuracy train accuracy test loss validation loss train loss
chest-x-ray.pt pneumonia classification using Multi Layer Perceprton (MLP) 73.73% 73.73% 72.47% 0.621 0.621 0.639

Classification report

This classification report is based on the first batch of the test dataset i used which consist of 64 images in a batch.

# precision recall f1-score support
micro avg 100% 81% 90% 4096
macro avg 100% 81% 90% 4096
weighted avg 100% 81% 90% 4096

Confusion matrix

The following image represents a confusion matrix for the first batch in the validation set which contains 64 images in a batch:

Pneumonia classification

If you hit the server at http://localhost:3001/api/pneumonia you will be able to get the following expected response that is if the request method is POST and you provide the file expected by the server.

Expected Response

The expected response at http://localhost:3001/api/pneumonia with a file image of the right format will yield the following json response to the client.

{
  "predictions": {
    "class_label": "PNEUMONIA VIRAL",
    "label": 2,
    "meta": {
      "description": "given a medical chest-x-ray image of a human being we are going to classify weather a person have pneumonia virus, pneumonia bacteria or none of those(normal).",
      "language": "python",
      "library": "pytorch",
      "main": "computer vision (cv)",
      "programmer": "@crispengari"
    },
    "predictions": [
      {
        "class_label": "NORMAL",
        "label": 0,
        "probability": 0.15000000596046448
      },
      {
        "class_label": "PNEUMONIA BACTERIA",
        "label": 1,
        "probability": 0.10000000149011612
      },
      { "class_label": "PNEUMONIA VIRAL", "label": 2, "probability": 0.75 }
    ],
    "probability": 0.75
  },
  "success": true
}

Using curl

Make sure that you have the image named normal.jpeg in the current folder that you are running your cmd otherwise you have to provide an absolute or relative path to the image.

To make a curl POST request at http://localhost:3001/api/pneumonia with the file normal.jpeg we run the following command.

curl -X POST -F [email protected] http://127.0.0.1:3001/api/pneumonia

Using Postman client

To make this request with postman we do it as follows:

  1. Change the request method to POST
  2. Click on form-data
  3. Select type to be file on the KEY attribute
  4. For the KEY type image and select the image you want to predict under value
  5. Click send

If everything went well you will get the following response depending on the face you have selected:

{
  "predictions": {
    "class_label": "NORMAL",
    "label": 0,
    "meta": {
      "description": "given a medical chest-x-ray image of a human being we are going to classify weather a person have pneumonia virus, pneumonia bacteria or none of those(normal).",
      "language": "python",
      "library": "pytorch",
      "main": "computer vision (cv)",
      "programmer": "@crispengari"
    },
    "predictions": [
      {
        "class_label": "NORMAL",
        "label": 0,
        "probability": 0.8500000238418579
      },
      {
        "class_label": "PNEUMONIA BACTERIA",
        "label": 1,
        "probability": 0.07000000029802322
      },
      {
        "class_label": "PNEUMONIA VIRAL",
        "label": 2,
        "probability": 0.07999999821186066
      }
    ],
    "probability": 0.8500000238418579
  },
  "success": true
}

Using JavaScript fetch api.

  1. First you need to get the input from html
  2. Create a formData object
  3. make a POST requests
res.json()) .then((data) => console.log(data));"> 
const input = document.getElementById("input").files[0];
let formData = new FormData();
formData.append("image", input);
fetch("http://127.0.0.1:3001/api/pneumonia", {
  method: "POST",
  body: formData,
})
  .then((res) => res.json())
  .then((data) => console.log(data));

If everything went well you will be able to get expected response.

{
  "predictions": {
    "class_label": "PNEUMONIA VIRAL",
    "label": 2,
    "meta": {
      "description": "given a medical chest-x-ray image of a human being we are going to classify weather a person have pneumonia virus, pneumonia bacteria or none of those(normal).",
      "language": "python",
      "library": "pytorch",
      "main": "computer vision (cv)",
      "programmer": "@crispengari"
    },
    "predictions": [
      {
        "class_label": "NORMAL",
        "label": 0,
        "probability": 0.15000000596046448
      },
      {
        "class_label": "PNEUMONIA BACTERIA",
        "label": 1,
        "probability": 0.10000000149011612
      },
      { "class_label": "PNEUMONIA VIRAL", "label": 2, "probability": 0.75 }
    ],
    "probability": 0.75
  },
  "success": true
}

Notebooks

The ipynb notebook that i used for training the model and saving an .pt file was can be found:

  1. Model Training And Saving
Owner
crispengari
ai || software development. (creating brains using artificial neural nets to make softwares that has human mind.)
crispengari
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