Differential diagnosis of Crohn’s disease and intestinal tuberculosis based on ATR-FTIR spectroscopy combined with machine learning

doi:10.3748/wjg.v30.i10.1377

Advanced Search

BPG is committed to discovery and dissemination of knowledge

Home / Archive / Volume 30, Issue 10

This Article

Academic Content and Language Evaluation of This Article

CrossCheck and Google Search of This Article

Academic Rules and Norms of This Article

Citation of this article

Corresponding Author of This Article

Research Domain of This Article

Article-Type of This Article

Open-Access Policy of This Article

Times Cited Counts in Google of This Article

Number of Hits and Downloads for This Article

Total Article Views (1688)

All Articles published online

The chart showing PDF series, WORD series, HTML series, Figures (1-11) series, Tables (1-4) series.

Item

Count

PDF

WORD

HTML

986

Figures (1-11)

173

Tables (1-4)

152

Sum=1390

Publishing Process of This Article

The chart showing Browse series, Download series.

Item

Count

Browse

Download

156

Sum=212

Mar 14, 2024 (publication date) through Aug 24, 2024

Times Cited of This Article

Times Cited (2)

Journal Information of This Article

Publication Name

World Journal of Gastroenterology

ISSN

1007-9327

Publisher of This Article

Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

Observational Study

World J Gastroenterol. Mar 14, 2024; 30(10): 1377-1392
Published online Mar 14, 2024. doi: 10.3748/wjg.v30.i10.1377

Full Size Figure Download Figure

Figure 3 Pathological images of Crohn’s disease and intestinal tuberculosis. A: Typical pathological images of Crohn’s disease (CD); B: Pathological images of typical intestinal tuberculosis (ITB); C: Atypical pathological images of CD; D: pathological images of atypical ITB.

Citation: Li YP, Lu TY, Huang FR, Zhang WM, Chen ZQ, Guang PW, Deng LY, Yang XH. Differential diagnosis of Crohn’s disease and intestinal tuberculosis based on ATR-FTIR spectroscopy combined with machine learning. World J Gastroenterol 2024; 30(10): 1377-1392
URL: https://www.wjgnet.com/1007-9327/full/v30/i10/1377.htm
DOI: https://dx.doi.org/10.3748/wjg.v30.i10.1377