X-ray-images-classification-with-Keras-TensorFlow

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X-ray-images-classification-with-Keras-TensorFlow

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ConvNet (CNN) implementation to classify x-ray medical images

X-ray-images-classification-with-Keras-TensorFlow 최신버젼 다운로드

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# NIH ChestXray14 image classification

## About
We are using the ChestXray14 raw chest x-ray dataset at [NIH Clinical Center](https://www.nih.gov/news-events/news-releases/nih-clinical-center-provides-one-largest-publicly-available-chest-x-ray-datasets-scientific-community). The code is inspired by the cat vs. dog image classificaton at [Machine Learning Crash Course by Google Developers](https://developers.google.com/machine-learning/practica/image-classification/).

## Requirements
Tested in Ubuntu 18.04.1 and MacOS:

1. Download image data from [ChestXray14 source](https://nihcc.app.box.com/v/ChestXray-NIHCC) and decompress.
2. Download and install [Anaconda](https://www.anaconda.com/distribution/#macos).
3. Start Anaconda Navigator.
4. Create TensorFlow environment (Tab "Environments").
5. Select TensorFlow environment and install: keras, tensorflow, matplotlib, nomkl, h5py, pillow and keras-metrics (pip install keras-metrics).
6. Install Jupyter Notebook (Tab "Home").

## Run
1. Activate TF environment (Tab "Environments").
2. Launch Jupyter Notebook (Tab "Home").
3. Open "CNN.ipynb"-file inside the Jupyter Notebook and run all cells starting at the top.

## Improve
1. Create Deep CNN (more layers).
2. Add different Dropout layers.
3. Less image-downscaling (variable: target_size).
4. Create Transfer learning CNN from InceptionV3 by cutting at 'mixed7' or last convolution layer (VGG, AlexNet, ResNet, DenseNet).
5. Use data augmentation to balance underrepresented classes.
6. Play with train/validation split.
7. Consideration of unbalanced dataset (class_weight='balanced')

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