title: An intelligent decision support system for quantitative assessment of gastric atrophy.
abstract: OBJECTIVE
  To build an automated decision support system to assist pathologists in grading gastric atrophy according to the updated Sydney system.
  
  
  METHODS
  A database of 143 biopsies was used to train and examine the proposed system. A panel of three experienced pathologists reached a consensus regarding the grading of the studied biopsies using the visual scale of the updated Sydney system. Digital imaging techniques were utilised to extract a set of discriminating morphological features that describe each atrophy grade sufficiently and uniquely. A probabilistic neural networks structure was used to build a grading system. To evaluate the performance of the proposed system, 66% of the biopsies (94 biopsy images) were used for training purposes and 34% (49 biopsy images) were used for testing and validation purposes.
  
  
  RESULTS
  During the training phase, a 98.9% precision was achieved, whereas during testing, a precision of 95.9% was achieved. The overall precision achieved was 97.9%.
  
  
  CONCLUSIONS
  A fully automated decision support system to grade gastric atrophy according to the updated Sydney system is proposed. The system utilises advanced image processing techniques and probabilistic neural networks in conducting the assessment. The proposed system eliminates inter- and intra-observer variations with high reproducibility.
authors.affiliation: Computer Engineering Department, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid, Jordan. fomari@yu.edu.jo
authors.forename: Faruq A
authors.initials: FA
authors.lastname: Al-Omari
authors.forename: Ismail I
authors.initials: II
authors.lastname: Matalka
authors.forename: Mohammad A
authors.initials: MA
authors.lastname: Al-Jarrah
authors.forename: Fatima N
authors.initials: FN
authors.lastname: Obeidat
authors.forename: Faisal M
authors.initials: FM
authors.lastname: Kanaan
mesh_terms: D001706:Biopsy; D016208:Databases, Factual; D003661:Decision Support Techniques; D005757:Gastritis, Atrophic / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D016571:Neural Networks, Computer; D015588:Observer Variation; D011706:Pyloric Antrum / Q000473:pathology*; D015203:Reproducibility of Results; D012720:Severity of Illness Index