# Quantification of Liver Fibrosis--A Comparative Study
## 3 Literature Review
### Aim of Study
#### 3.1.4 Intelligent Diagnostic Systems for Automated CPA Detection  
We are moving into a new era, where the application of machine learning algorithms is spreading for solving complex problems. Starting with [PERSON] et al. [50], they presented a novel and intelligent automated quantification system (AQS) using a multilayer feed-forward backpropagation artificial neural network (MLFFBP-ANN). [PERSON] et al. [51] relied on a combined approach based on second-harmonic generation/two-photon excitation fluorescence (SHG/TPEF) imaging for detecting changes in collagen deposition.  
On the other hand, [PERSON] et al. [52] presented a fully-automated assessment method called \"qFibrosis,\" which was based on extensive image processing and quantification of portal, septal and fibrillar collagen deposition. In [PERSON] et al. [53], the research team employed the fully automated qFibrosis system to distinguish the changes among the portal, septal and fibrillar collagen features.  
As stated in [PERSON] et al. [54], small changes in liver fibrosis may be overlooked in clinical trials, leading to invalid diagnostic outcomes. For this reason, they developed a fully automated method for providing objective hepatitis B-related fibrosis quantitative measurements. Collagen was detected using SHG microscopy in unstained liver biopsies, while hepatocyte morphology was recorded using TPEF microscopy.  
A method based on DIA was designed by [PERSON] et al. [55] based on image segmentation and Bayesian classification techniques to determine the differences between fibrosis regions and healthy tissue. [PERSON] and [PERSON] [56] focused on implementing a fibrosis detection system based on an automated feature extractor using linear genetic programming (LGP).  
In [PERSON] et al. [57], an automated diagnostic method based on clustering was proposed for the extraction of the image of the tissue and the detection of fibrosis in CHC biopsy specimens. Clustering refers to a more sophisticated process of image segmentation, as opposed to thresholding techniques that were used for separating the background from the tissue pixels, as well as for the detection of fibrosis pixels in liver tissue. [PERSON] et al. [58] introduced a fully automated methodology for the extraction of CPA and the classification of different tissue regions. In [PERSON] et al. [59], a modified clustering algorithm was proposed for the classification of CPA through segmentation.  
The research community is currently experiencing a revolution in the field of machine learning with the advent of deep neural networks (DNNs). Deep learning, in general, is based on the idea that algorithms can be trained without the extensive need for the user's interference. This approach has offered the medical field more automated solutions to the identification of complex structures that characterize severe pathological conditions. Many researchers have been inspired by the above, including [PERSON] et al. (2019) for fully automating the fibrosis detection procedure with classical computer vision techniques (image processing, conventional machine learning) and convolutional neural networks (CNNs). A CNN model was also employed in [PERSON] et al. (2019) for the identification of fibrotic areas in liver biopsy specimens. At a later stage, the team made a comparison of the deep model's performance with other conventional classification algorithms.