Spanish Chinese Russian German French Japanese Portuguese Hindi Telugu Tamil
Flyer

Archives of Clinical Microbiology

  • ISSN: 1989-8436
  • Journal h-index: 24
  • Journal CiteScore: 8.01
  • Journal Impact Factor: 7.55
  • Average acceptance to publication time (5-7 days)
  • Average article processing time (30-45 days) Less than 5 volumes 30 days
    8 - 9 volumes 40 days
    10 and more volumes 45 days
Awards Nomination 20+ Million Readerbase
Google Scholar citation report
Citations : 2234

Archives of Clinical Microbiology received 2234 citations as per Google Scholar report

Flyer image
Indexed In
  • Open J Gate
  • Genamics JournalSeek
  • The Global Impact Factor (GIF)
  • Open Archive Initiative
  • China National Knowledge Infrastructure (CNKI)
  • Directory of Research Journal Indexing (DRJI)
  • OCLC- WorldCat
  • Proquest Summons
  • Publons
  • MIAR
  • University Grants Commission
  • Geneva Foundation for Medical Education and Research
  • Euro Pub
  • Google Scholar
  • Scimago Journal Ranking
  • Secret Search Engine Labs
  • ResearchGate
  • International Committee of Medical Journal Editors (ICMJE)
View More
Share This Page

Image recognition techniques on digital images of colon and stomach biopsies

4th International Conference on Digital Pathology
August 22-23, 2019 Zurich, Switzerland

Pascale De Paepe, Kenny Goossens, Ward Van Laer, Wim De Clercq and Katrien De Wolf

Departement of Pathological Anatomy AZ Sint-Jan Brugge ��? Oostende AV | Campus Brugge,Europe Ixor, cvba Schuttersvest 75 2800 Mechelen, Europe

Posters & Accepted Abstracts: Arch Clin Microbiol

Abstract:

By using Machine Learning (ML) techniques it is possible to recognize patterns in digital images and to classify these images based on their contents with high accuracy. Pattern recognition is one of the main parameters used by pathologists in the analysis of biopsy material. We therefore focused our study on pattern recognition and not on object (cell) detection/ classification. The aim of our study is to build and train one algorithm which will pre-analyse digital images of different types of intestinal mucosa.
Digital images from gastric (51) and colon (92) mucosal biopsies were labeled normal or abnormal. Images of gastric biopsies were labeled as abnormal when following histological features were present: increased number of inflammatory cells, interstitial oedema and differentiation abnormalities of the epithelial lining. Images of colon biopsies were labeled as abnormal when distortion of the glands, villous structures, differentiation abnormalities of the epithelial lining, increased number of inflammatory cells were found. All images showing no abnormalities were labeled as normal. With these data sets we trained different machine learning algorithms to classify the digital images. The best performing algorithm, a support vector machine classifier, achieved an accuracy of 94% on colon images and 75% on gastric images. This pilot study illustrates the possibility to train an algorithm on a limited data set so that it classifies with acceptable accuracy. If the algorithm proves to be as successful on full scanned biopsies it will be helpful as a pre-analysis tool in daily histopathological workload.

Biography :

E-mail:

Pascale.DePaepe@azsintjan.be

PDF HTML