Automated interpretation of biopsy images for the detection of celiac disease using a machine learning approach

Koh, Joel En Wei and de Michele, Simona and Sudarshan, Vidya K. and Jahmunah, V. and Ciaccio, Edward J. and Ooi, Chui Ping and Gururajan, Raj ORCID: https://orcid.org/0000-0002-5919-0174 and Gururajan, Rashmi and Oh, Shu Lih and Lewis, Suzanne K. and Green, Peter H. and Bhagat, Govind and Acharya, U. Rajendra (2021) Automated interpretation of biopsy images for the detection of celiac disease using a machine learning approach. Computer Methods and Programs in Biomedicine, 203:106010. ISSN 0169-2607


Abstract

Background and objectives: Celiac disease is an auto immune disease occurring in about 1 in 100 people worldwide. Early diagnosis and efficient treatment are crucial in mitigating the complications that areas-associated with untreated celiac disease, such as intestinal lymphoma and malignancy, and the subsequent high morbidity. The current diagnostic methods using small intestinal biopsyhis to pathology, endoscopy, and video capsule endoscopy(VCE) involve manual interpretation of photo micro graphsor images, which can be time-consuming and difficult, with inter-observer variability. In this paper, a machine learning technique was developed for the automation of biopsy image analysis to detect and classify villousatrophy based on modified Marsh scores. This is one of the first studies to employ conventional machine learning to automate the use of biopsy images for celiac disease detection and classification.


Statistics for USQ ePrint 41780
Statistics for this ePrint Item
Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Business (18 Jan 2021 -)
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Business (18 Jan 2021 -)
Date Deposited: 15 Apr 2021 02:14
Last Modified: 22 Sep 2021 05:32
Uncontrolled Keywords: Celiac disease; Biopsy images; Non linear features; Machine learning; Steerable pyramid transform; Image analysis Classifiers
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460299 Artificial intelligence not elsewhere classified
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Socio-Economic Objectives (2020): 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence
Identification Number or DOI: https://doi.org/10.1016/j.cmpb.2021.106010
URI: http://eprints.usq.edu.au/id/eprint/41780

Actions (login required)

View Item Archive Repository Staff Only