Image Classifier for Chest X-Ray Diagnosis
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README.md

Image Classification with CoreML2 for iOS

Introduction

With the advent of CoreML2 for iOS, Machine Learning has become more accessible, with simple models easily trainable on most laptops. This project demonstrates a simple modification of the model to be targeted towards diagnosis of pneumonia on a chest x-ray.

Initialisation

To start this project, open the ChestXray.xcodeproj file using XCode. Note - this will require MacOS Mojave to be installed with the latest version of XCode, else CoreML will fail to run.

Training a model

To train your own model - this can be done in XCode Playgrounds. Start a new Playground and run the following code to bring up a live view of the ML builder.

import CreateMLUI

let builder = MLImageClassifierBuilder()

builder.showInLiveView()

Changing the model

The model used can be easily changed for another with a simple modification to the code. In ImageClassificationViewController.swift change let model = try VNCoreModel(for: Pneumonia().model) with Pneumonia() being substituted for your model name. Ensure that your package is appropriately signed to prevent warnings and build-time errors.

Next Steps

  • Re-training the model (at present, issues exist with overfitting of the model), and broaden the scope to a larger range of conditions.
  • Allow for a more user friendly UI with visualisation of previous analyses.