## 3 Results
### Performance Comparison with Pre-Trained CNN Models  
In the next step, the performance of the optimal \(\mathrm{CNN}_{\mathrm{SGDM}}\) classifier is compared with two of the most widely used CNN pre-trained models. These refer to the 8-layer AlexNet [21] and the deeper 16-layer VGG-16 [22] neural networks, two architectures that have yielded high classification results in recent years. In both pre-trained models, transfer learning updates have been applied to adapt them to the current classification problem of the four liver tissue alterations. Initially, to apply transfer learning to the AlexNet network, the biopsy image patches were resized to \(227\times 227\times 3\) pixels, a necessary step to fit as input samples. The output layers of the original AlexNet-CNN network were replaced accordingly to generate probabilities for the four histological structures. In the case of VGG-16, the samples were converted to \(224\times 224\times 3\) pixel size and the output layers were modified as before.  
In both classifiers, the training process was set for a maximum of 10 epochs, while the SGDM algorithm was used to optimize the training process. The validation patience number was set again equal to 3, with the networks completing their training in less than 10 epochs. According to Table 2, a comparison of the accuracy, precision (positive predictive value--PPV), recall (sensitivity) and specificity (true negative rate--TNR) rates, generated by the AlexNet, VGG-16 and the previously built \(\mathrm{CNN}_{\mathrm{SGDM}}\) architecture, is done. According to the percentages in Table 2, the constructed \(\mathrm{CNN}_{\mathrm{SGDM}}\) architecture achieves better classification performances (accuracy: 95%, precision: 95%, recall: 95%, specificity: 98.3%) than VGG-16 (accuracy: 94%, precision: 94.1%, recall: 94%, specificity: 98%), while AlexNet emerges as the most optimal classifier (accuracy: 97%, precision: 97%, recall: 97%, specificity: 99%). All performance differences are presented in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Deep Model**} & \multicolumn{4}{c}{**Classification Results (\%)**} \\ \cline{2-5}  & **Accuracy** & **Precision** & **Recall** & **Specificity** \\  & **(PPV)** & **(Sensitivity)** & **(TNR)** \\ \hline \(\mathrm{CNN}_{\mathrm{SGDM}}\) & 95 & 95 & 95 & 98.3 \\ AlexNet & 97 & 97 & 97 & 99 \\ VGG-16 & 94 & 94.1 & 94 & 98 \\ \hline \multicolumn{5}{c}{**Conventional**} & \multicolumn{1}{c}{**Accuracy**} & **Precision** & **Recall** & **Specificity** \\ \multicolumn{5}{c}{**Model**} \\ \hline MLP-ANN & 90.3 & 90.3 & 90.3 & 96.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of \(\mathrm{CNN}_{\mathrm{SGDM}}\) performance with (a) pre-trained CNN models and (b) a conventional MLP neural network classifier.  
Figure 5: Display of various liver structure predictions. The figure includes random image patch classifications accompanied by their estimated probability distribution value (%). All of the misclassifications (marked in red) include sinusoids that refer to complex histological structures that should be excluded as disease findings in future object detection methodologies.