Automated interpretation of biopsy images for the detection of celiac disease using a machine learning approach
Article
Article Title | Automated interpretation of biopsy images for the detection of celiac disease using a machine learning approach |
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ERA Journal ID | 5039 |
Article Category | Article |
Authors | Koh, Joel En Wei (Author), de Michele, Simona (Author), Sudarshan, Vidya K. (Author), Jahmunah, V. (Author), Ciaccio, Edward J. (Author), Ooi, Chui Ping (Author), Gururajan, Raj (Author), Gururajan, Rashmi (Author), Oh, Shu Lih (Author), Lewis, Suzanne K. (Author), Green, Peter H. (Author), Bhagat, Govind (Author) and Acharya, U. Rajendra (Author) |
Journal Title | Computer Methods and Programs in Biomedicine |
Journal Citation | 203 |
Article Number | 106010 |
Number of Pages | 11 |
Year | 2021 |
Publisher | Elsevier |
Place of Publication | Ireland |
ISSN | 0169-2607 |
1872-7565 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.cmpb.2021.106010 |
Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0169260721000857?via%3Dihub |
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. |
Keywords | Celiac disease; Biopsy images; Non linear features; Machine learning; Steerable pyramid transform; Image analysis Classifiers |
ANZSRC Field of Research 2020 | 460299. Artificial intelligence not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions |
Institution of Origin | University of Southern Queensland |
Byline Affiliations | Ngee Ann Polytechnic, Singapore |
Columbia University, United States | |
Singapore University of Social Sciences (SUSS), Singapore | |
School of Business | |
Department of Health, Queensland | |
School of Business | |
Asia University, Taiwan | |
Kumamoto University, Japan |
https://research.usq.edu.au/item/q64w1/automated-interpretation-of-biopsy-images-for-the-detection-of-celiac-disease-using-a-machine-learning-approach
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