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

Last update: Jan 08, 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

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

Owner

crispengari

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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

Owner

crispengari

Deep Ensemble Learning with Jet-Like architecture

Unofficial implement with paper SpeakerGAN: Speaker identification with conditional generative adversarial network

DR-GAN: Automatic Radial Distortion Rectification Using Conditional GAN in Real-Time

Image marine sea litter prediction Shiny

PAMI stands for PAttern MIning. It constitutes several pattern mining algorithms to discover interesting patterns in transactional/temporal/spatiotemporal databases

The official pytorch implementation of our paper "Is Space-Time Attention All You Need for Video Understanding?"

The repo for reproducing Seed-driven Document Ranking for Systematic Reviews: A Reproducibility Study

A Domain-Agnostic Benchmark for Self-Supervised Learning

Implementation of Squeezenet in pytorch, pretrained models on Cifar 10 data to come

https://sites.google.com/cornell.edu/recsys2021tutorial

[ACM MM2021] MGH: Metadata Guided Hypergraph Modeling for Unsupervised Person Re-identification

This repository contain code on Novelty-Driven Binary Particle Swarm Optimisation for Truss Optimisation Problems.

Official code release for "GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis"

Cl datasets - PyTorch image dataloaders and utility functions to load datasets for supervised continual learning

A Confidence-based Iterative Solver of Depths and Surface Normals for Deep Multi-view Stereo

A PyTorch implementation of Sharpness-Aware Minimization for Efficiently Improving Generalization

Source Code For Template-Based Named Entity Recognition Using BART

DeLighT: Very Deep and Light-Weight Transformers

A scikit-learn compatible neural network library that wraps PyTorch

A GUI to automatically create a TOPAS-readable MLC simulation file

Using `curl`

Using JavaScript `fetch` api.