## Materials and Methods
### Convolutional Neural Network Model Construction  
In this stage, a CNN topology is defined to learn the most informative features from the extracted biopsy tissue structures. The convolution layer operations are accelerated with the use of an NVIDIA GTX1050 Ti graphics processing unit (GPU). This refers to a popular computing distribution technique that can train deep neural networks in a short time. Figure 3 displays the techniques used in each layer of the proposed CNN architecture.  
Figure 3: The proposed CNN architecture. It consists of an input layer of \(64\times 64\times 3\) image size, 3 convolution layers and a fully connected layer comprising of a dense layer of 4096 units. Finally, a softmax activation function generates the classification probabilities for each of the four hepatic classes (ballooning, fat, sinusoid, vein).  
Initially, in the input layer, each image patch is resized to \(64\times 64\times 3\) pixel size (width, height, depth), with the bicubic interpolation method. Since this generates a large number of connections weights for modeling the image data, several dimensionality reduction techniques are used in the subsequent convolution layers:  
* In the first convolution layer, 64 convolution filters consisting of a 5-by-5 kernel size are defined to detect \"low-level\" features, such as edges, from the raw image data. In each convolution operation, zero-padding is utilized to assign 0 values around the inputs to maintain an output size equal to the input of each kernel filter [14]. Subsequently, batch normalization is applied to normalize the convolved values, as well as the Rectified Linear Unit (ReLU), being the nonlinear activation function, which is considered ideal for minimizing the vanishing gradient problem [15]. Even though ReLUs are widely used in most deep learning applications, their unboundedness on the positive side tends to cause overfitting. To circumvent this issue, max pooling filtering with a stride of 2 is set to decrease overfitting by reducing the spatial size (width and height) of the data representation [16].
* The second convolution layer applies 32 filters with a 3-by-3 kernel size to search for \"higher-level\" features within each liver tissue object, including hepatocytes within a ballooning area, as well as multiple occurring pixels pointing at blood cells in hepatic veins. Batch normalization, ReLU function, and max pooling are included again, while dropout with a 0.5 probability is applied with the purpose to prevent overfitting [17].
* In the third convolution layer, 16 filters with a 3-by-3 kernel size aim to emphasize on connected pixels that can differentiate the textural features among the four examined histological structures. Max pooling is no longer applied and the training process makes a transition to the fully connected layer.
* The fully connected layer defines a dense layer with 4096 flattened neurons to gather the filtered anatomical features from the three convolution layers. These neurons are further connected to the final softmax layer. Dense and softmax layer connections act similar to a multilayer perceptron (MLP) artificial neural network, with the softmax function allocating probability distributions during the prediction of the four hepatic classes [18].