Detection of fibrosis in liver biopsy images using multi-objective genetic programming

Thong-on, Purit; Watchareeruetai, Ukrit

doi:10.1109/iciteed.2017.8250486

Cited by 3 publications

(7 citation statements)

References 20 publications

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“…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.…”

Section: Cpa Results Produced By Automated Intelligent Systemsmentioning

confidence: 99%

“…In recent years, the employment of machine learning techniques has flourished as a necessary computer vision tool, but the published methodologies [55][56][57][58][59] were evaluated using a relatively limited dataset (8-79 biopsy specimens). This is due to the fact that the semi-quantitative assessment and the annotation of various histological structures is a time-consuming but also a necessary process for the employment of each method.…”

Section: Number Of Histological Samples In Later Methodologiesmentioning

confidence: 99%

“…A subset of 10 images was used to train a Bayesian classifier, whilst 24 additional images were used to test the accuracy of the proposed method. Thong-on and Watchareeruetai's [56] biopsy image analysis prioritized the segmentation of fibrous tissue to determine the severity of liver damage. To achieve this goal, all experiments have been conducted with a dataset including 10 training and 22 test images.…”

Section: Number Of Histological Samples In Later Methodologiesmentioning

confidence: 99%

“…A method based on DIA was designed by Meejaroen et al [55] based on image segmentation and Bayesian classification techniques to determine the differences between fibrosis regions and healthy tissue. Thong-on and Watchareeruetai [56] focused on implementing a fibrosis detection system based on an automated feature extractor using linear genetic programming (LGP).…”

Section: Intelligent Diagnostic Systems For Automated Cpa Detectionmentioning

confidence: 99%

“…The Evolutionary computation (EC) at Thong-on and Watchareeruetai [56] utilized the Genetic Algorithm II (NSGA-II) algorithm to ensure the survival of an elite group of offspring population. Initially, the crossover operation was performed after selecting a random pair of programs as parents.…”

Section: Application Of Automated Fibrosis Detection Techniquesmentioning

confidence: 99%

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Quantification of Liver Fibrosis—A Comparative Study

et al. 2020

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Liver disease has been targeted as the fifth most common cause of death worldwide and tends to steadily rise. In the last three decades, several publications focused on the quantification of liver fibrosis by means of the estimation of the collagen proportional area (CPA) in liver biopsies obtained from digital image analysis (DIA). In this paper, early and recent studies on this topic have been reviewed according to these research aims: the datasets used for the analysis, the employed image processing techniques, the obtained results, and the derived conclusions. The purpose is to identify the major strengths and “gray-areas” in the landscape of this topic.

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Section: Cpa Results Produced By Automated Intelligent Systemsmentioning

confidence: 99%

Section: Number Of Histological Samples In Later Methodologiesmentioning

confidence: 99%

Section: Number Of Histological Samples In Later Methodologiesmentioning

confidence: 99%

Section: Intelligent Diagnostic Systems For Automated Cpa Detectionmentioning

confidence: 99%

Section: Application Of Automated Fibrosis Detection Techniquesmentioning

confidence: 99%

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Quantification of Liver Fibrosis—A Comparative Study

et al. 2020

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A Review of Genetic Programming: Popular Techniques, Fundamental Aspects, Software Tools and Applications

Ari

Alagöz

2021

Sakarya University Journal of Science

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Genetic Programming (GP) is one of the evolutionary computation (EC) methods followed with great interest by many researchers. When GP first appeared, it has become a popular computational intelligence method because of its successful applications and its potentials to find effective solutions for difficult practical problems of many different disciplines. With the use of GP in a wide variety of areas, numerous variants of GP methods have emerged to provide more effective solutions for computation problems of diverse application fields. Therefore, GP has a very rich literature that is progressively growing. Many GP software tools developed along with process of GP algorithms. There is a need for an inclusive survey of GP literature from the beginning to today of GP in order to reveal the role of GP in the computational intelligence field. This survey study aims to provide an overview of the growing GP literature in a systematic way. The researchers, who need to implement GP methods, can gain insight of potentials in GP methods, their essential drawbacks and prevalent superiorities. Accordingly, taxonomy of GP methods is given by a systematic review of popular GP methods. In this manner, GP methods are analyzed according to two main categories, which consider the discrepancies in their program (chromosome) representation styles and their methodologies. Besides, GP applications in diverse problems are summarized. This literature survey is especially useful for new researchers to gain the required broad perspective before implementing a GP method in their problems.

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Fuzzy Logic-Based System for Liver Fibrosis Disease

Alkhalifah¹,

Singla²,

Alurise³

2023

CSSE

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The diagnosis of liver fibrosis (LF) is crucial as it is a deadly and life-threatening disease. Artificial intelligence techniques aid doctors by using the previous data on health and making a diagnostic system, which helps to take decisions about patients' health as experts can. The historical data of a patient's health can have vagueness, inaccurate, and can also have missing values. The fuzzy logic theory can deal with these issues in the dataset. In this paper, a multilayer fuzzy expert system is developed to diagnose LF. The model is created by using multiple layers of the fuzzy logic approach. This system aids in classifying the health of patients into different classes. The proposed method has two layers, i.e., layer 1 and layer 2. The input variables used in layer 1 for diagnosing liver fibrosis are Appetite, Jaundice, Ascites, Age, and Fatigue. Similarly, in layer 2, the input variables are Platelet count, White blood cell count, spleen, SGPT ALT (Serum Glutamic Pyruvic Transaminase Alanine Aminotransferase), SGOT ALT (Serum Glutamicoxalacetic Transaminase Alanine Aminotransferase), Serum bilirubin, and Serum albumin. The output variables for this developed system are no damage, minimal damage, significant damage, severe damage, and cirrhosis. This research work also presents the examination of results based on performance parameters. The proposed system achieves a classification accuracy of 95%. Moreover, other performance parameters such as sensitivity, specificity, and precision are calculated as 97.14%, 92%, and 94.44%, respectively.

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Detection of fibrosis in liver biopsy images using multi-objective genetic programming

Cited by 3 publications

References 20 publications

Quantification of Liver Fibrosis—A Comparative Study

Quantification of Liver Fibrosis—A Comparative Study

A Review of Genetic Programming: Popular Techniques, Fundamental Aspects, Software Tools and Applications

Fuzzy Logic-Based System for Liver Fibrosis Disease

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