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

crispengari

Last update: Jan 8, 2022

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.

pneumonia bacteria
pneumonia virus
normal

Starting the server

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

create a virtual environment and activate it
run the following command to install packages

pip install -r requirements.txt

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:

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

First you need to get the input from html
Create a formData object
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:

Model Training And Saving

You might also like...

LF-YOLO (Lighter and Faster YOLO) is used to detect defect of X-ray weld image.

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

Overview

Pneumonia Classification

Starting the server

Model

Model Metrics

Classification report

Confusion matrix

Pneumonia classification

Expected Response

Using `curl`

Using Postman client

Using JavaScript `fetch` api.

Notebooks

You might also like...

LF-YOLO (Lighter and Faster YOLO) is used to detect defect of X-ray weld image.

Medical-Image-Triage-and-Classification-System-Based-on-COVID-19-CT-and-X-ray-Scan-Dataset

Normal Learning in Videos with Attention Prototype Network

Code Release for ICCV 2021 (oral), "AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds"

Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation

An implementation of a discriminant function over a normal distribution to help classify datasets.

Code of Adverse Weather Image Translation with Asymmetric and Uncertainty aware GAN

Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions

Pytorch Lightning Distributed Accelerators using Ray

Owner

crispengari

TorchXRayVision: A library of chest X-ray datasets and models.

Computational modelling of ray propagation through optical elements using the principles of geometric optics (Ray Tracer)

Shuwa Gesture Toolkit is a framework that detects and classifies arbitrary gestures in short videos

Flower classification model that classifies flowers in 10 classes made using transfer learning (~85% accuracy).

Pneumonia Detection using machine learning - with PyTorch

CasualHealthcare's Pneumonia detection with Artificial Intelligence (Convolutional Neural Network)

This is the official Pytorch implementation of "Lung Segmentation from Chest X-rays using Variational Data Imputation", Raghavendra Selvan et al. 2020

Official code of "R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network."

automated systems to assist guarding corona Virus precautions for Closed Rooms (e.g. Halls, offices, etc..)

This is a custom made virus code in python, using tkinter module.

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

Overview

Pneumonia Classification

Starting the server

Model

Model Metrics

Classification report

Confusion matrix

Pneumonia classification

Expected Response

Using curl

Using Postman client

Using JavaScript fetch api.

Notebooks

You might also like...

LF-YOLO (Lighter and Faster YOLO) is used to detect defect of X-ray weld image.

Medical-Image-Triage-and-Classification-System-Based-on-COVID-19-CT-and-X-ray-Scan-Dataset

Normal Learning in Videos with Attention Prototype Network

Code Release for ICCV 2021 (oral), "AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds"

Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation

An implementation of a discriminant function over a normal distribution to help classify datasets.

Code of Adverse Weather Image Translation with Asymmetric and Uncertainty aware GAN

Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions

Pytorch Lightning Distributed Accelerators using Ray

Owner

crispengari

TorchXRayVision: A library of chest X-ray datasets and models.

Computational modelling of ray propagation through optical elements using the principles of geometric optics (Ray Tracer)

Shuwa Gesture Toolkit is a framework that detects and classifies arbitrary gestures in short videos

Flower classification model that classifies flowers in 10 classes made using transfer learning (~85% accuracy).

Pneumonia Detection using machine learning - with PyTorch

CasualHealthcare's Pneumonia detection with Artificial Intelligence (Convolutional Neural Network)

This is the official Pytorch implementation of "Lung Segmentation from Chest X-rays using Variational Data Imputation", Raghavendra Selvan et al. 2020

Official code of "R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network."

automated systems to assist guarding corona Virus precautions for Closed Rooms (e.g. Halls, offices, etc..)

This is a custom made virus code in python, using tkinter module.

Using `curl`

Using JavaScript `fetch` api.