# Quantification of Liver Fibrosis--A Comparative Study
## 4 Results and Discussion
### Results from Earlier Works Employing DIA and HAI Systems
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author/Year** & **Dataset/Histological Stain** & **HAI** & **Image Analysis Method** & **Results/Remarks** \\ \hline \multirow{4}{*}{[PERSON] et al. 2008 [PERSON] et al. 2008 [46]} & \multirow{4}{*}{210 samples (CLD patients), 50 normal samples} & \multirow{4}{*}{[PERSON]} & Contrast enhancement. & \multirow{4}{*}{AGS testing accuracy: 94.69\%, \textless{} 194\%} \\  & & Stain: [PERSON] & & K-means clustering. & \multirow{4}{*}{AGS-Ishak correlations (Ph1:} \\  & & ANN classification (MATLAB 6.5) & 0.889, Ph2. 0.815) \\ \hline \multirow{4}{*}{[PERSON] et al. 2014 [PERSON] et al. 2014 [51]} & 200 samples & \multirow{4}{*}{METAVR} & SHG/TPEF image preprocessing. & \multirow{4}{*}{Mean PR-area \(\leq 0.9719\)} \\  & & & K-means clustering. & \\  & & BoF, k-NN classification & & \\ \hline \multirow{4}{*}{[PERSON] et al. 2014 [PERSON] et al. 2014 [52]} & \multirow{4}{*}{162 samples (CHB patients), 25 TA-induced rats} & \multirow{4}{*}{METAVR, Ishak} & Automated SHG/TPEF image preprocessing. & \multirow{4}{*}{METAVR: animals} \\  & & & ML classification (9 fibrosis) & (AUC \(\leq 0.99\), \(r<0.001\)), \\  & & & & humans (AUC \(\leq 0.97\), \(r<0.05\)) \\ \hline \multirow{4}{*}{[PERSON] et al. 2018 [PERSON] et al. 2018 [53]} & \multirow{4}{*}{162 samples (CHB patients)} & \multirow{4}{*}{Ishak (modified)} & Automated SHG/TPEF image preprocessing. & \multirow{4}{*}{GI fibrosis-Ishak correlation:} \\  & & Stain: H& MET, reticulin (modified) & & Feature selection, model fitting \\  & & & (MATLAB 2015) & & \(r<0.001\)) \\ \hline \multirow{4}{*}{[PERSON] et al. 2015 [PERSON] et al. 2015 [54]} & \multirow{4}{*}{175 samples (CHBV patients)} & \multirow{4}{*}{Ishak} & Binary image conversion. & \multirow{4}{*}{(AUC \(\leq 0.82\))} \\  & & Stain: MT (at a later stage) & & Collagen area segmentation & \\  & & & GLM, SVM classification & 0.745, 1-2 vs. 3.6 = 0.774, 1-3 & \\  & & & & vs. 4-6 = 0.797 \\ \hline \multirow{4}{*}{[PERSON] et al. 2015 [55]} & \multirow{4}{*}{34 samples (fibrosis patients)} & \multirow{4}{*}{-} & Image enhancement. & \multirow{4}{*}{Classification accuracy =} \\  & & & Low-pass filtering. & & 91.42\% \\ \cline{1-1}  & & & Bayesian classification & \\ \hline \multirow{4}{*}{Thong-on and Watcharewneuteli 2017 [56]} & \multirow{4}{*}{32 samples (fibrosis patients)} & \multirow{4}{*}{-} & MOCP Segmentation (NSGA-II). & \multirow{4}{*}{NCGA-II segmentation} \\  & & & Bayesian classification & \\ \cline{1-1}  & & & & accuracy = 92.33\%, \\ \cline{1-1} \cline{2-2} \cline{4-4}  & 25 samples (CHC patients) & \multirow{4}{*}{[PERSON]} & Manual fibrotic area annotation. & \multirow{4}{*}{CFA segmentation error} \\  & & Stain: picro-Srius Red & K-means color image clustering & & rates \(\leq 2.9\%\) \\ \hline \multirow{4}{*}{[PERSON] et al. 2017 [58]} & \multirow{4}{*}{79 samples (HCV patients)} & \multirow{4}{*}{-} & K-means, Fuzzy Cmeans clustering. & \multirow{4}{*}{Mean CPA classification error} \\  & & Stain: picro-Srius red & & KNN, C45, RS, MLP, SVM fibrosis & \multirow{4}{*}{(abs) \(\geq 1.3\%\), \\  & & & regions classification & \\ \hline \multirow{4}{*}{[PERSON] et al. 2017 [PERSON] et al. 2017 [59]} & \multirow{4}{*}{8 samples (HCV patients)} & \multirow{4}{*}{-} & K-means algorithm & Mean classification \\  & & & (parameterized) image clustering. & \multirow{4}{*}{1/2 of image dataset error \(<1\%\)} \\  & & & Bayesian classification & \\ \hline \multirow{4}{*}{[PERSON] et al. 2017 [PERSON] et al. 2017 [60]} & \multirow{4}{*}{107 samples (fibrosis and steatosis prevalence)} & \multirow{4}{*}{-} & Image preprocessing, & \multirow{4}{*}{GBT, SVM, LR, RF, CNN classification.} & \multirow{4}{*}{R\({}^{2}\leq 89.3\) 1  } \\  & & & C-test Segmentation & & \\ \hline \multirow{4}{*}{[PERSON] et al. 2018 [PERSON] et al. 2018 [61]} & \multirow{4}{*}{25 samples (21 TA-induced rats and 4 as controls)} & \multirow{4}{*}{METAVR} & SHG contrast enhancement. & \multirow{4}{*}{AUROC: CNN \(\leq 0.95\),} \\  & & & Morphological closing. & & ANN \(\leq 1.00\), \\ \cline{1-1}  & Stain: picro-Srius red (at a & & ANN, MLS, SVM, RF, & \\ \cline{1-1}  & & & AlexNet-CNN classification & \\ \cline{1-1}  & & & RF \(\leq 0.99\) \\ \hline \hline \end{tabular}
\end{table}