# Medinoid: Computer-Aided Diagnosis and Localization of Glaucoma Using Deep Learning +
## 2 Methodology
### Convolutional Neural Networks  
Given their high effectiveness, CNNs are currently the most widely-used technique in image classification [30; 31; 32]. Their strength stems from the use of convolutional filters that are composed of neurons, also known as receptive fields. Inspired by biological processes, the neurons convolve over local regions in the input layer and respond to certain patterns, as the visual cortex does to stimuli in a local space. This is an important characteristic because, unlike the full connectivity of ordinary neural networks, this local connectivity enables CNNs to handle high-dimensional inputs such as natural images by substantially reducing the number of parameters to compute. In addition, CNNs are able to learn and classify hierarchical features directly from raw input images (end-to-end learning), thus not needing extraction of hand-crafted features. As CNNs have been demonstrated to be highly effective across various general image classification tasks, including the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC), which comes with more than 1.2 millions images distributed over 1000 classes [33], medical image analysis using CNNs followed quickly [34], targeting use cases such as diabetic retinopathy diagnosis [35] and chest pathology analysis [36; 37]. To achieve our goal of effective glaucoma diagnosis, we explored three representative CNNs: (1) VGG Networks (VGGNets) [38], (2) Inception Networks (InceptionNets) [31], and (3) Residual Networks (ResNets) [39]. Compared with their predecessors such as LeNetand AlexNet, these networks are all based on deeper architectures, all achieving higher accuracies while maintaining lower error rates. A comparison of the accuracies achieved by the different types of networks can be found in Table 2.  
The aforementioned CNNs were able to obtain high levels of accuracy thanks to the availability of ImageNet. This implies that, to achieve similar levels of effectiveness for our use case of glaucoma diagnosis, we need a dataset of similar size to ImageNet or we need to modify the CNNs in question so that they are able to mitigate overfitting issues caused by the usage of a limited amount of data [34]. As such, starting from the CNNs in question, we incorporated several regularization techniques, including early stopping, data augmentation, dropout layers, and transfer learning [41], with the aim of achieving better model generalization. In what follows, we explain the main characteristics of each model, with Table 3 summarizing the modifications we made. **VGGNets:** Developed at and named after Oxford's Visual Geometry Group (VGG), VGGNets [38] were the first models to combine small receptive fields with a size of \(3\times 3\) with multiple CNN layers. VGGNets are constructed using several convolutional blocks that contain two to three convolutional layers, followed by a max pooling layer. VGG-16 is currently the most representative network, having a relatively low number of parameters, but a higher top 1 classification accuracy compared to VGG-19. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **Top 1 Accuracy** & **Top 5 Accuracy** \\ \hline VGG-16 & 71.5 & 89.8 \\ VGG-19 & 71.1 & 89.8 \\ Inception-v4 & 80.2 & 95.2 \\ Inception-ResNet-v2 & 80.4 & 95.3 \\ ResNet-152 & 77.8 & 94.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracies of different deep neural network models trained on the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC)-2012-CLS. Top 1 accuracy: the class having the highest probability is the same as the true label. Top 5 accuracy: one class among the top 5 ranked classes (ranked according to decreasing probability) is the same as the true label [40]. Like ResNets, VGGNets require input images to be normalized, resulting in zero mean and unit variance. Furthermore, VGGNets and ResNets make use of the same input size. VGG-16 in its original form uses Stochastic Gradient Descent (SGD) as an optimizer, also employing learning rate decay.