Review for 10.1186/s12876-020-01494-7 (2020)

Reviewer’s report
Title: 
Deep Learning for Sensitive Detection of Helicobacter Pylori in Gastric
Biopsies
Version: 
3 
Date: 
22 Sep 2020
Reviewer's report:
PEER REVIEWER ASSESSMENTS: 
OBJECTIVE - Full research articles: is there a clear objective that addresses one
or several testable research questions? (Brief or other article types: is there a
clear objective?)
Yes - there is a clear objective
DESIGN - Is the current approach (including controls and analysis protocols)
appropriate for the objective?
Yes - the approach is appropriate
EXECUTION - Are the experiments and analyses performed with sufficient
technical rigor to allow confidence in the results?
Yes - experiments and analyses were performed appropriately
STATISTICS - Is the use of statistics in the manuscript appropriate?
Yes - appropriate statistical analyses have been used in the study
INTERPRETATION - Is the current interpretation/discussion of the results
reasonable and not overstated?
Yes - the author's interpretation is reasonable
OVERALL MANUSCRIPT POTENTIAL - Has the author addressed your
concerns sufficiently for you to now recommend the work as a technically sound
contribution? If not, can further revisions be made to make the work technically
sound?
Yes - current version is technically sound
PEER REVIEWER COMMENTS: 
GENERAL COMMENTS: The paper has been revised based on the reviewers
comments
REQUESTED REVISIONS: 
Figure 5: The architecture of the ANN in Figure 5 is not that of CNN depict the
proper architecture similar to figure 1
The Artificial Intelligence 
in the title of the paper is not proper "Artificial
Intelligence for Sensitive Detection of Helicobacter Pylori in Gastric Biopsies"
because Artificial Intelligence is too broad. The title should carry the algorithm
used in the paper that's is the variant of convolutional neural network adopted in
the study.
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