# Quantification of Liver Fibrosis--A Comparative Study
## 4 Results and Discussion
### CPA Results Produced by Automated Intelligent Systems  
The overall testing accuracy in [PERSON] et al. [50] was 94.69%. Also, its correlation with the semi-quantitative Ishak scoring of the two physicians was acceptable (Ph1: 0.889, Ph2: 0.815). In [PERSON] et al. [51], the classification of DSIFT-BoF was evaluated by calculating the under Precision-Recall (PR) curves area. When various bin sizes were applied, an optimal PR value in METAVIR stage 0_S images was observed, with a mean PR-area of 0.9719. It was made clear that a 6-pixel bin size provided satisfactory mean PR-area values, especially in the case of METAVIR S012_vs_S34 (0.7948) and METAVIR S0123_vs_S4 (0.9451) images. The [PERSON] et al. [52] fully automated method identified differences between all METAVIR stages in both animal samples (\(p\) < 0.001) and human biopsies (\(p\) < 0.05). qFibrosis was able to differentiate Ishak stages 5 and 6 (AUC = 0.73, \(p\) = 0.008) and was able to predict staging underestimation in suboptimal biopsies (<15 mm) and under/over-scoring by different pathologists (\(p\) < 0.001). In [PERSON] et al. [53], the qFibrosis results correlated well with liver stiffness (r = 0.6, \(p\) < 0.001). Following antiviral treatment, a decline in inflammation prevalence was observed, with LSM significantly decreasing and significantly correlating with the qFibrosis diagnosis (r = 0.3, \(p\) < 0.001). A receiver operating characteristic (ROC) curve was used in [PERSON] et al. [54] for the evaluation of SHG/TPEF fibrosis scoring performance. Overall, the non-linear SVM model was superior to the generalized linear model (GLM). In more detail, the multivariate generalized linear model was able to perform an optimal separation between non-cirrhotic (Ishak stages 1-4) and cirrhotic (Ishak 5-6) patients with AUC = 0.829. However, the SVM model was optimal in classifying early fibrotic stages: (a) Ishak 1 vs. 2-6: AUC = 0.745, (b) Ishak 1-2 vs. 3-6: AUC = 0.774, and (c) Ishak 1-3 vs. 4-6: AUC = 0.797. The experimental results of [PERSON] et al. [55] showed that an 8\({}^{3}\) bin value combined with a 7 \(\times\) 7 average filtering gave the best performance, yielding a fibrosis classification accuracy of 91.42%. In addition, the average differences between the fibrosis percentage obtained from the proposed method and that of ground truth images were 2.29 points. The evolutionary method of Thong-on and Watchareeruetai [56] was executed 10 times with the same parameter setting. The fibrosis estimation error was measured as the absolute difference between the percentage of segmented fibrosis and the corresponding ground truth, determined by other methods. Of all generated individuals, the optimum yield had a fibrosis segmentation accuracy of 93.05 and a corresponding error of 2.09, which was less than the handcraft method (error = 2.63). Concerning the rest of the individuals, about half of them were able to achieve better results, while the rest had similar error range and compared to a Bayesian classification approach, which showed accuracy and error rates of 92.33 and 2.63, respectively. After the study by [PERSON] et al. [57], computed CPA errors were found to be less than 1% in most images. However, there were three cases (from 25 in total) where the calculated CPA from the proposed method greatly differed from the value provided by the physician. In all other cases, error rates ranged from 0.05% to 2.93%. In [PERSON] et al. [58], all CPA results derived from the proposed methodology were compared to those defined from the experts' annotations for each clustering/classification algorithm, with the calculation of the absolute CPA error, defined as  
\[\text{AE}_{\text{CPA}}(\%)=|\text{CPA}_{\text{annot}}-\text{CPA}_{\text{ method}}|, \tag{12}\]  
where the mean absolute CPA error for all biopsy images was finally calculated, as well as the absolute CPA error standard deviation.