Visually Significant Cataract AI Model Test

AI classification model for Visually Significant Cataract prediction

Prerequisite

Hardware Resource Recommendations

• CPU: Intel Core or Xeon Serial 64 bits Processors (released in recent years)
• Memory: More than 16G
• Disk: More than 20G free space
• GPU: Not necessary

User

A sudo user is required for running commands in following sections.

Operating System

Recommend to use Ubuntu 18.04 LTS (64 bits), Ubuntu 16.04 LTS (64 bits), Ubuntu 20.04 LTS or later versioin. The code also can run on the virtual environment in other Linux, windows and Mac OS operation system.

System should be updated to latest version:

sudo apt-get update sudo apt-get upgrade -y

Software

Reqired System Software Packages

Recommend to use python3.7 environment, the code can run with python3.6 and python3.8 environment. sudo apt-get install -y python3.7 python-pip python3.7-tk tk-dev build-essential swig libsm6 libxrender1 libxext-dev pip recommend to be upgraded to latest version: pip install --upgrade pip If this is the 1st time to upgrade pip as normal user, logout and login will be required in order to use the new version pip installed in user home directory.

Required Python Packages

All required packages with specific versions are listed in file requirements.txt, run command to install: pip install -r requirements.txt You can also use virtual environment to setup the working environment, run command to install: sudo apt-get install python3-venv apt-get install python3.7-dev python3.7-venv python3 -m venv env source env/bin/activate pip install --upgrade pip pip install -r requirements.txt

Dataset

Please copy you validation fundus image files to one folder, e.g. ./images. The supported image file format includes: png, bbmp, jpg, or tiff.

Prediction

Usage

``` usage: python3 main.py --input DATASET_DIR [--output OUTPUT_DIR] [--threshold THRESHOLD_VALUE] [-h]

options: --input DATASETDIR The input directory for dataset image files, must be specified. --output OUTPUTDIR The result output csv file directory, optional, default to ./outputs. -h Show command line options.

examples: git clone https://github.com/SunnyAVT/visuallysignificantcataract.git cd visuallysignificantcataract python3 main.py python3 main.py --input ./images --output ./outputs python3 main.py --input ./yourimagefolder --output ./youroutputfolder ```

Result

The prediction result will be shown at the end of the program stdout. The result will be also stored in a file with name TestResult.csv in OUTPUT_DIR.

Example

There are 4 sample images under ./images directory for demo purpose: ```

python3.7 main.py --input ./images --output ./outputs Using Theano backend. loading model: paperretinaref048Resnet50cataractmodelp3.dat loading paperretinaref048Resnet50cataractmodelp3.dat model, time 0.03

100%|##########################################################| 4/4 [00:10<00:00, 3.30s/it] filename:['201696189L.jpg'], probability0:0.9404348134994507, probability1:0.05956516042351723 filename:['201698588R.jpg'], probability0:0.8374420404434204, probability1:0.1625579446554184 filename:['201699522L.jpg'], probability0:0.5960693359375, probability1:0.4039306938648224 filename:['201696189R.jpg'], probability0:0.8547605872154236, probability1:0.14523939788341522 Cataract Validation is Over, please get your results in outputs/TestResult.csv !!! ``` Note: You can replace the images in ./images directory with your test retinal fundus images, then run the same command as above for the AI cataract prediction.

Trouble shooting

1) Because of the difference in the OS, installation package or running environment, user could encounter error in setup pip install -r requirements.txt You can use try different packages module version as below. Such as, you can use tensorflow==1.15 to replace tensorflow==1.14.0 Such as, you can use numpy==1.20.1 to replace numpy==1.19.5

2) Don’t need to care about the warning message in the running, it will not affect the final results. The reason is that the code stick to use tensorflow 1.x version in order to compatible with some AI libs. resnet50.py:60: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(2048, (1, 1), name="res5b_branch2c")` x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c')(x) resnet50.py:51: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (1, 1), name="res5c_branch2a")` x = Convolution2D(nb_filter1, 1, 1, name=conv_name_base + '2a')(input_tensor) resnet50.py:56: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (3, 3), name="res5c_branch2b", padding="same")` border_mode='same', name=conv_name_base + '2b')(x) resnet50.py:60: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(2048, (1, 1), name="res5c_branch2c")`

Citation (TBD)

Please cite our paper if you find this code useful in your research. The APA entry for the paper is: Tham, Y. C., Goh, J. H. L., Anees, A., Lei, X, et al., Detecting Visually Significant Age-Related Cataract using Retinal Photograph-Based Deep Learning: Development, Validation, and Comparison with Clinical Experts (Version 2.0.4) [Computer software]. https://doi.org/10.xxx/zenodo.xxxx The BibTeX entry for the paper is: @software{Tham_Detecting_Visually_Significant, author = {Tham, Yih Chung and Goh, Jocelyn Hui Lin and Anees, Ayesha and Lei, Xiaofeng, et al.}, doi = {10.5281/zenodo.xxxx}, title = {{Detecting Visually Significant Age-Related Cataract using Retinal Photograph-Based Deep Learning: Development, Validation, and Comparison with Clinical Experts}}, url = {https://github.com/SunnyAVT/visually_significant_cataract}, version = {2.0.4} }

/etc/apt/sources.list

```

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```