CheXNet-Keras | build CheXNet-like models | Machine Learning library

by brucechou1983 Python Version: Current License: MIT

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kandi X-RAY | CheXNet-Keras Summary

CheXNet-Keras is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Keras applications. CheXNet-Keras has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

ChexNet is a deep learning algorithm that can detect and localize 14 kinds of diseases from chest X-ray images. As described in the paper, a 121-layer densely connected convolutional neural network is trained on ChestX-ray14 dataset, which contains 112,120 frontal view X-ray images from 30,805 unique patients. The result is so good that it surpasses the performance of practicing radiologists. If you are new to this project, Luke Oakden-Rayner's post is highly recommended.

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CheXNet-Keras has a low active ecosystem.

It has 273 star(s) with 144 fork(s). There are 24 watchers for this library.

It had no major release in the last 6 months.

There are 15 open issues and 26 have been closed. On average issues are closed in 7 days. There are 5 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of CheXNet-Keras is current.

Quality

CheXNet-Keras has 0 bugs and 0 code smells.

Security

CheXNet-Keras has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

CheXNet-Keras code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

CheXNet-Keras is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

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CheXNet-Keras releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions are available. Examples and code snippets are not available.

Top functions reviewed by kandi - BETA

kandi has reviewed CheXNet-Keras and discovered the below as its top functions. This is intended to give you an instant insight into CheXNet-Keras implemented functionality, and help decide if they suit your requirements.

Create a CAM overlay
Load an image
Return the output layer corresponding to the given name
Calculate ROCAUC score
Return the training data
Prepare the next epoch
Prepare the dataset
Create a Keras model
Calculates the weights for each class
Get the sample counts for each class
Return y data

Get all kandi verified functions for this library.

CheXNet-Keras Key Features

No Key Features are available at this moment for CheXNet-Keras.

CheXNet-Keras Examples and Code Snippets

No Code Snippets are available at this moment for CheXNet-Keras.

Community Discussions

Trending Discussions on CheXNet-Keras

How can I insert a scalar value and a binary value to a layer (last layer) in keras?

QUESTION

How can I insert a scalar value and a binary value to a layer (last layer) in keras?

Asked 2019-May-27 at 06:24

I am trying to modify the network which is implemented here. This network uses chest x ray images as input and classifies it into 14 categories (13 types of diseases and no finding). The network does not take the patient age and gender as an input. So I want to provide the network with that information too. In short At the last 3 layers of the network is like the following:

...

ANSWER

Answered 2019-May-24 at 09:36

You can have a multi input model.

So instead of just using this:

Source https://stackoverflow.com/questions/56276064

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install CheXNet-Keras

Download all tar files, Data_Entry_2017.csv and BBox_List_2017.csv of ChestX-ray14 dataset from NIH dropbox. Put them under ./data folder and untar all tar files.
Create & source a new virtualenv. Python >= 3.6 is required.
Install dependencies by running pip3 install -r requirements.txt.
Copy sample_config.ini to config.ini, you may customize batch_size and training parameters here. Make sure config.ini is configured before you run training or testing
Run python train.py to train a new model. If you want to run the training using multiple GPUs, just prepend CUDA_VISIBLE_DEVICES=0,1,... to restrict the GPU devices. nvidia-smi command will be helpful if you don't know which device are available.
Run python test.py to evaluate your model on the test set.
Run python cam.py to generate images with class activation mapping overlay and the ground bbox. The ground truth comes from the BBox_List_2017.csv file so make sure you have that file in ./data folder. CAM images will be placed under the output folder.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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