A Self-Adaptive Deep Learning Method for Automated Eye Laterality Detection Based on Color Fundus Photography

<p>The data for &quot;A Self-Adaptive Deep Learning Method for Automated Eye Laterality Detection Based on Color Fundus Photography&quot;, including original image data and labels, DL model files, training logs and validation results.</p> <p><strong>Content</strong>:</p> <p>- <strong>Ex1_Preprocessing</strong> # the first experiment: preprocessing methods comparison</p> <p>&nbsp; &nbsp; - <strong>history</strong> # validation results of different metrics of models&nbsp;</p> <p>&nbsp; &nbsp; - <strong>log</strong> # training record of models. Read by TensorBoard</p> <p>&nbsp; &nbsp; - <strong>model</strong> # model file.&nbsp; Read by Keras</p> <p>- <strong>Ex2_Self-adaptive model</strong> # the second experiment: development of eye laterality detection model</p> <p>&nbsp; &nbsp; - <strong>data</strong> # serialized image data, including data and ground-truth label. Read by Python Pickle package.</p> <p>&nbsp; &nbsp; &nbsp; &nbsp; - <strong>Xy.h5</strong> # training data</p> <p>&nbsp; &nbsp; &nbsp; &nbsp; - <strong>test_Xy.h5</strong> # testing data</p> <p>&nbsp; &nbsp; - <strong>history</strong> # validation results of different metrics of models&nbsp;</p> <p>&nbsp; &nbsp; - <strong>log</strong> # training record of models. Read by TensorBoard</p> <p>&nbsp; &nbsp; - <strong>model</strong> # model file. Read by Keras</p> <p>&nbsp; &nbsp; &nbsp; &nbsp; - <strong>with_adaptive</strong> # with self-adaptive strategy</p> <p>&nbsp; &nbsp; &nbsp; &nbsp; - <strong>without_adaptive</strong> # without self-adaptive strategy</p> <p>For code review, please see: <a href="https://github.com/keepgallop/Eye-laterality-detection">https://github.com/keepgallop/Eye-laterality-detection</a></p> <p>For more information, please contact liuchi_email@foxmail.com</p> <p>&nbsp;</p> <p>&nbsp;</p> <p>&nbsp;</p>