Glaucoma image detection based on convolutional neural networks.
This repository has the structure:
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Processor.py- Class that implements some image filter signal processing techniques. -
Interpreter.py- Class that implements data augmentation procediment and CNN processing based on traditional approach and window optimization technique. -
test_processor.py- Implements unit tests related toProcessor.pyclass methods. -
test_processor.py- implements training proccess. -
Graphs.py- Class that implements graphs visualization. -
train_model.ipynb- Notebook responsible to train simplified model and and Resnet-20 at AWS-Sagemaker. -
train_transf_net.ipynb- Notebook responsible to train EfficientNet-B0 until EfficientNet-B7 at AWS-Sagemaker.
Besides of that, into the folder AWS_EvalTest_Results you can find some approaches to automatization of experiments related to tunning hyperparameters.
[1] Attila Budai, Joachim Hornegger, Georg Michelson: Multiscale Approach for Blood Vessel Segmentation on Retinal Fundus Images. In Invest Ophthalmol Vis Sci 2009;50: E-Abstract 325, 2009.
[2] C. Pena-Betancor, M. Gonzalez-Hernandez, F. Fumero-Batista, J. Sigut, E. Mesa, S. Alayon, and M. G. de la Rosa, "Estimation of the relative amount of hemoglobin in the cup and neuro-retinal rim using stereoscopic color fundus images," IOVS, pp. IOVS–14–15592, Feb. 2015.
[3] Zhang, Zhuo, et al. "Origa-light: An online retinal fundus image database for glaucoma analysis and research." 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. IEEE, 2010.
[4] Lee, Hyunkwang, Myeongchan Kim, and Synho Do. "Practical window setting optimization for medical image deep learning." arXiv preprint arXiv:1812.00572 (2018).
[5] Thomas Köhler, Attila Budai, Martin Kraus, Jan Odstrcilik, Georg Michelson, Joachim Hornegger. Automatic No-Reference Quality Assessment for Retinal Fundus Images Using Vessel Segmentation, 26th IEEE Internatioal Symposium on Computer-Based Medical Systems 2013, Porto