# Medinoid: Computer-Aided Diagnosis and Localization of Glaucoma Using Deep Learning +
## 3 Experimental Setup
### Implementation  
Our approach towards glaucoma diagnosis and localization was implemented in Python 2.7, using Tensorflow 1.7, with all pre-trained models taken from Tensorflow Slim ([[https://github.com/tensorflow/models/tree/master/research/slim](https://github.com/tensorflow/models/tree/master/research/slim)]([https://github.com/tensorflow/models/tree/master/research/slim](https://github.com/tensorflow/models/tree/master/research/slim))). Furthermore, our approach was run using two Intel(R) Xeon(R) E5-2620 2.4-GHz CPUs and an NVIDIA GeForce GTX TITAN X GPU.  
We focused on regularizing and generalizing the different predictive models created by making use of different techniques, for instance mitigating overfitting and bias by adding a dropout layer as a regularizer at the end of each convolutional block, residual block, or inception block, depending on the type of model used. Our overall experimental setup can be found in Table 3.  
**Data pre-processing:** In this phase, all the input images were re-sized and normalized before training, taking into account the pre-trained model settings [31; 38; 39]. For VGG-16-* and ResNet-152-B, we re-sized the input to \(224\times 224\times 3\) and normalized the input using the following RGB means: R:105.51, G:54.52, and B:16.19 (as derived from the images in the training set). For ResNet-152-Mand Inception-v4-*, after re-sizing the input to \(299\times 299\times 3\), we scaled the input values to the range \([-1,\,1]\) instead of \([0,\,255]\).  
**Data augmentation:** Overfitting occurs frequently when combining a small-sized dataset with a deep neural network, as was the case for the given set of fundus images and the architectures used, with the latter coming with a large number of parameters that needed to be computed. In order to alleviate overfitting, we incorporated data augmentation, artificially amplifying the volume of data during training through image processing techniques (e.g., horizontal flipping, contrast adjustment, and brightness adjustment), and where these techniques are often used in the context of computer vision tasks. Based on a comparison of the accuracy of different data augmentation techniques in [48], we applied several random data augmentation methods, also taking into account the different models used (that is, for each model, we used different types of data augmentation).  
**Fine-tuning:** When training from scratch, a model often initializes all parameters using random Gaussian distributions. This approach towards initialization is usually less accurate, with the model at hand needing more time to converge [34; 49]. To avoid initialization issues, we employed models pre-trained on ImageNet, transferring their weights to our models, hereby also allowing for better generalization. Starting from these pre-trained weights, we fine-tuned all layers using the  
Figure 2: Taking into account the clinical importance of the Optic Disc (OD) and the macula, the original dataset was used to created two additional datasets, either by center-cropping the original images (Dataset-C) or by extracting the optic disc from the original images (Dataset-OD).  
hyperparameter values shown in Table 4, over Dataset-C and Dataset-OD. Note that the batch size used was 32.