## 4 Discussion
### Qualitative Performance Comparison with Prior Methodologies
Binary segmentation of the red, green and blue (RGB) channels resulted in the distinction of fat vacuoles and the measurement of the fat proportionate area (mFPA). The results showed that there were significant increases in alanine aminotransferase (ALT), and aspartate aminotransferase (AST) when the fat content increased. Other data also indicated both 5% and 20% of mFPA as a cut-off for raised ALT. Moreover, significant growth in hepatic architecture (HA) and lobule radius (LR) were observed when fat accumulation increased (mFPA = 10%). [PERSON] et al. [9] proposed a segmentation method for extracting histological regions of interest from high-resolution biopsy images. This was followed by the application of image enhancement and morphological operation techniques to enhance and smooth the boundaries of steatosis components, as well as to remove small undesired objects. Furthermore, a sophisticated technique for assigning curvature points to differentiate overlapped fat droplets was presented. Finally, a supervised classification step was used and resulted in discrimination rates for both isolated and overlapped steatosis, where in most cases they were equal to 100%. Following [PERSON] et al. [10], they focused on a multiple liver class recognition problem. The method relied on both image preprocessing and supervised classification techniques. The SVM algorithm performed 89% classification accuracy and identified macrosteatosis, bile ducts, portal veins and sinusoids with precision and recall values \(\geq\) 82%. The same team in a subsequent study [12] focused on the automatic detection and quantification of lobular inflammation and hepatocellular ballooning. As before, image preprocessing and supervised classification resulted in 70% and 49% precision and recall values for lobular inflammation and 91% and 54% for hepatocellular ballooning. In addition, the classifier had a 95% area under the curve (AUC) for lobular inflammation and 98% for hepatocellular ballooning. The Spearman's correlation coefficient was applied to compare the method's performance with that of expert pathologists and was 45.2% for lobular inflammation and 46% for hepatocyte ballooning. [PERSON] et al. [11] applied a three-dimensional imaging technique, to generate spatially-resolved geometrical and functional models for the diagnosis of liver tissue specimens at different NAFLD stages. The methodological approach identified a set of morphological changes associated with NAFLD progression. These morphological changes included the size of lipid droplets distribution, nuclear texture homogeneity, and feature values which were used as tissue biomarkers to distinguish the different stages of NAFLD progression. Correlations between various diagnosed findings and NAFLD progression in individual patients were analyzed. The results indicated that gamma-glutamyl transpeptidase (GGT) had the strongest Pearson correlation coefficient (GGT = 0.680). The same coefficient was 0.473 for alkaline phosphatase (ALP), 0.505 for total bile acid (BA) and 0.518 for the corresponding primary BA. More recently, there has been an increase in the selection of deep convolutional neural networks for the classification and monitoring of microscopic structures. [PERSON] et al. [13] aimed at fully-automating the liver fibrosis detection process. The same group also focused on the objective quantification of steatosis, with classical computer vision techniques (image processing, conventional machine learning) and CNNs being the two diagnostic approaches. In the case of deep neural networks, the U-net proved to be the optimal approach for performing pixel-wise region segmentations. The validation of the automated quantitative analysis was performed using the R\({}^{2}\) correlation coefficient based on a physician's qualitative scores. Specifically, the R\({}^{2}\) was 0.748 for the classical computer vision approach and 0.893 for the CNN, respectively.