# Classification of OCT Images of the Human Eye Using Mobile Devices
## 2 Materials and Methods
### Selection of Neural Network Models for OCT Image Classification  
Six different CNNs were selected for training and testing during the experiments:  
* **CNN with 3 convolutional layers** --A simple CNN with 3 convolutional layers was trained and tested, before analyzing more advanced, pre-trained neural networks with more convolutional layers. Its structure is listed in Table 3. Each 2D convolution layer had a \(3\times 3\) kernel, stride of 1, no padding, and a ReLU activation function. Additionally, after the first convolutional layer, a max pooling operation was applied to reduce dimensionality by half. The introduction of dropout after each convolutional layer is aimed at reducing overfitting. The final classification probability was obtained after applying flattening and two fully connected layers. * **VGG16**--A high-classification efficiency network, considered one of the better image processing models currently available [14]. The model creators increased the depth of this network by using small (3 \(\times\) 3) convolutional filters, resulting in a model with multiple convolutional layers and input data with a size of \(224\times 224\times 3\). * **InceptionV3**--A deep CNN. It was created in 2015 as a variant of the GoogLeNet network, where subnetworks called inception modules were introduced, allowing for a greater network expansion compared to previous varieties. The network consists of 42 layers [15], uses the max-pooling operation, batch normalization, and the auxiliary classifiers method, i.e., additional classifiers that help in effectively transferring information during the learning process. The whole contributes to using a minimum number of parameters, resulting in reduced computational costs and thus achieving better performance. * **Xception**--Xception is a CNN from the GoogLeNet family, designed in 2016 [16]. It uses concepts derived from GoogLeNet and ResNet. However, the inception modules \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Resolution [px]** & **CNV** & **DME** & **DRUSEN** & **NORMAL** & **VMT** & **Total** \\ \hline
384 \(\times\) 496 & 2 & - & - & - & - & 2 \\
512 \(\times\) 496 & 296 & 121 & 403 & 120 & - & 940 \\
512 \(\times\) 512 & - & 571 & - & 77 & - & 648 \\
583 \(\times\) 1010 & - & - & - & - & 2 & 2 \\
600 \(\times\) 1009 & - & - & - & 1 & - & 1 \\
768 \(\times\) 496 & 380 & 122 & 310 & 62 & - & 874 \\
799 \(\times\) 1010 & - & - & - & - & 3 & 3 \\
800 \(\times\) 1009 & - & - & - & - & 2 & 2 \\
800 \(\times\) 1010 & - & - & - & 111 & 54 & 165 \\
900 \(\times\) 1010 & - & - & - & - & 23 & 23 \\
998 \(\times\) 960 & - & - & - & 2 & - & 2 \\
1020 \(\times\) 768 & - & - & - & 326 & 456 & 782 \\
1020 \(\times\) 960 & - & - & 54 & 186 & 404 & 644 \\
1024 \(\times\) 496 & 6 & - & - & - & - & 6 \\
1536 \(\times\) 496 & 207 & 44 & 88 & 34 & - & 373 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sizes of images in OCT2023 dataset. mentioned in the Inception architecture have been replaced with a unique type of layer called a depthwise separable convolution (DSC) layer, which has become a key element of the network. The DSC layer consists of two parts. In the first part, a single spatial filter is used for each input feature map, while in the second part, only inter-channel patterns are searched, which is the opposite of a classical convolutional network, where filters try to detect spatial and inter-channel patterns in parallel. * **Joint Attention Network + MobileNetV2--**The Joint Attention Network (JAN) consists of an encoder connected to a MobileNetV2 architecture classifier and an unsupervised decoder that does not have access to the true image classes from the training set. This structure achieves high network accuracy for validation and test data [17]. * **OpticNet-71--**A neural network (first described in 2019 [18]) designed specifically for analyzing OCT images, especially in the context of diagnosing eye diseases such as glaucoma and diabetic retinopathy.