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