## 4 Discussion
### Qualitative Performance Comparison with Prior Methodologies  
The number of digitally scanned microscopic specimens (64), along with the extracted image patches (720), form a sufficient image dataset for the implemented deep CNN architecture. A step that allows a qualitative comparison of the present work with recent innovative efforts aimed at locating diverse anatomical structures and chronic conditions, exclusively on liver biopsy images. Unfortunately, a direct quantitative comparison with other relative works, presented in the literature, is not feasible. The current study employs a unique liver biopsy dataset, while the cited papers do not in all cases analyze the same histological areas of interest. Also, the cited methods do not rely on similar evaluation metrics when measuring their classification capability. Therefore, Table 4 intends to make a qualitative comparison of the referred methodologies, derived from a combination of digital image processing techniques with conventional machine learning algorithms and fully automated deep learning architectures. The table below initially shows that image preprocessing to H&E stained samples remains an essential step for image segmentation purposes. Also, unsupervised machine learning algorithms, such as K-means, refer to popular clustering techniques for separating biopsy samples from their background as well as tissue structures of interest. Subsequently, these methods lead well-known classifiers such as k-nearest neighbors (k-NN), decision tree (DT) and support vector machines (SVM) to high object recognition performances. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Deep Model** & **Trainable Parameters** & \
\begin{tabular}{c} **Training “from Scratch”** \\ **(Minutes)** \\ \end{tabular} &
\begin{tabular}{c} **Transfer Learning** \\ **(Minutes)** \\ \end{tabular} \\ \hline \(\mathrm{CNN}_{\mathrm{SGDM}}\) & 16,825,876 & 2 & - \\ AlexNet & 60,000,000 & - & 0.45 \\ VGG-16 & 138,000,000 & - & 5.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Indirect comparison of training times for the three deep models in the current liver biopsy dataset. The term indirect is emphasized since only the \(\mathrm{CNN}_{\mathrm{SGDM}}\) model is trained from scratch with the liver biopsy dataset. Transfer learning is used to train the other two models (AlexNet and VGG-16). [PERSON] et al. [6] presented an image analysis method that could distinguish the main differences between small-droplet macrovesicular steatosis (sd-Mas) and large-droplet macrovesicular steatosis (ld-MaS). The methodology was based on an automated active contour modeling (ACM) technique for lipid droplet segmentation, the unsupervised K-means algorithm for clustering the two objects of interest and a decision tree classifier to improve the separation between the two categories. After the classification stage, specificity and sensitivity values were 93.7% and 99.3%, respectively. The \begin{table}
\begin{tabular}{c c c c c} \hline \hline  
* Confidence intervals (with 95% confidence). 2 Correlation coefficient with human expert quantification.
* Performance from the constructed CNN model employing the Adam and SGDM optimizers.  
\end{table} 
Table 4: Qualitative comparison with recent years image analysis methods from the literature. linear regression coefficient of determination was equal to 0.97, as the correlation method with semi-quantitative pathologists' assessments. [PERSON] et al. [7] proposed an image processing-based method for estimating the fat ratio in liver biopsy images. This study adopted classic image processing techniques to extract the areas of candidate fat blobs, including grayscale and binary image conversion, background segmentation and average noise filtering. Next, the k-NN classifier was called to identify fat blobs that could lead to an accurate calculation of the fat prevalence ratio. [PERSON] et al. [8] investigated the relationships between liver fat, aminotransferases, and hepatic architecture in stenotic liver sample examinations.