An evolutionary algorithm-based optimization method for the classification and quantification of steatosis prevalence in liver biopsy images - Arjmand, Alexandros; Christou, Vasileios; Tsoulos, Ioannis G.; Tsipouras, Markos G.; Tzallas, Alexandros T.; Gogos, Christos; Glavas, Euripidis; Giannakeas, Nikolaos (Array (New York), 2021)
Non-alcoholic fatty liver disease (NAFLD) covers a range of chronic medical conditions varying from hepatocellular inflammation which characterizes nonalcoholic steatohepatitis (NASH) to steatosis, as the key element of a nonalcoholic fatty liver (NAFL). It is globally linked to the increasing prevalence of obesity and other components of metabolic syndrome and is expected to be the main indication for the existence of the liver disease in the coming years. Its eradication has become a major challenge due to the difficulties in clinical diagnosis, complex pathogenesis and the lack of approved therapies. In this study, an automated image analysis method is presented providing quantitative assessments of fat deposition in steatotic liver biopsy specimens. The methodology applies image processing, machine learning and evolutionary algorithm optimization techniques, producing a 1.93% mean classification error compared to the semiquantitative interpretations of specialized hepatologists. •An automated diagnostic tool for the accurate steatosis prevalence quantification in NAFLD biopsy images.•Image processing analysis for determining the liver tissue area and detecting circular objects of interest.•Machine Learning versus Deep Learning in Biopsy Image Analysis.