# Fundus Laterality #
### Laterality Classification of Fundus Images using Interpretable Deep Neural Networks [[original paper]()] [[cite]()]###
## Package Dependency ##
matplotlib==1.3.1
numpy==1.14.3
scikit_image==0.10.1
pandas==0.21.1
scipy==0.19.1
Keras==2.1.6
Pillow==5.1.0
skimage==0.0
scikit_learn==0.19.1
## Running the code ##
At the home directory, install required packages with
``` pip install requirements.txt```
Run inferece with
``` python inference.py --test_dir=<folder_path> --gpu_index=<gpu_index>```
For instance, run inferece for the sample dataset ([IDRiD dataset](https://idrid.grand-challenge.org/)) with
``` python inference.py --test_dir=IDRiD_imgs/ --gpu_index=0```
IDRiD_NAR_013.jpg is wrongly classified in the sample dataset.
## File Hierarchy ##
.
├── IDRiD_imgs
├── inference.py
├── model
│ └── relu_softmax_weight_epoch49.h5
├── network.py
├── README.md
├── requirements.txt
├── results.csv
└── utils.py
IDRiD_imgs: sample fundus images
inference.py: inference file to run
model/relu_softmax_weight_epoch49.h5: weight file
network.py: network file in keras style
results.csv: results of laterality with columns of filename and the results (R or L)
utils.py: miscellaneous helper functions
## LICENSE ##
This is under the MIT License
Copyright (c) 2018 Vuno Inc. (www.vuno.co)
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温馨提示
本项目旨在利用深度神经网络对眼底图像进行侧向性分类。眼底图像侧向性分类对于眼科疾病的早期诊断具有重要意义。 我们采用卷积神经网络(CNN)等深度学习算法,通过分析眼底图像,实现对侧向性的自动识别和分类。项目使用的数据集包括公开的眼底图像数据集,如DIARETDB1、DRIVE等,并进行了预处理,包括图像增强、分割和特征提取等。 在运行环境方面,我们使用Python编程语言,基于TensorFlow、PyTorch等深度学习框架进行开发。为了提高模型的性能,我们还使用了数据增强、模型融合等技术。 项目完成后,将实现对眼底图像的侧向性分类,为眼科医生提供有力的辅助诊断工具,有助于提高患者的生活质量。同时,项目成果也可应用于其他医学图像分析领域。
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