Title: Establishing an AI Model and Application for Automated Capsule Endoscopy
Recognition Based on Convolutional Neural Networks (with Video)
Version: 1 Date: 19 Sep 2024
Reviewer's report:
The work on automated capsule endoscopy recognition is highly impressive and demonstrates a
significant advancement in the field of small bowel lesion diagnostics. However, there are still
some issues in the article.
1. There is no numbering provided to the section and sub-sections, so that it will be more easy
to refer and understand.
2. A statistical graph or figure can be included in the introduction section to give a pictorial idea
about the present scenario of gastrointestinal diseases throughout the world.
3. A paragraph needs to be added in introduction, stating the objective and major contribution
(in bullet points).
4. A section needs to be added for elaborating some prior state of work on this area of research.
5. The dataset section needs to be more elaborated, giving a detailed about the dataset that is
considered in this work.
6. Could you elaborate on why you chose these CNN models - DenseNet121, EfficientNetB2,
HRNet-W18, ResNet50, and MobileNetv3-large?
7. Do add detailed explanations of each performance metric, particularly the specific meaning
and application scenarios of accuracy, precision, recall, specificity, AUC and F1 score.
8. Figure 3 should be referred at the beginning of the methods section so that the reader can get
a pictorial idea about the whole work from initial phase.
9. The section heading of “Application Development” is repeated.
10. To enhance the quality of the paper, a critical discussion can be added mentioning the pros
and cons of the proposed work.
11. Go through the article thoroughly for grammatical and typological errors.
12. Re-write the conclusion so as to incorporate an overall outcome of the study, limitations
and future scope.
Title: Establishing an AI Model and Application for Automated Capsule Endoscopy
Recognition Based on Convolutional Neural Networks (with Video)
Version: 1 Date: 19 Sep 2024
Reviewer's report:
The work on automated capsule endoscopy recognition is highly impressive and demonstrates a
significant advancement in the field of small bowel lesion diagnostics. However, there are still
some issues in the article.
1. There is no numbering provided to the section and sub-sections, so that it will be more easy
to refer and understand.
2. A statistical graph or figure can be included in the introduction section to give a pictorial idea
about the present scenario of gastrointestinal diseases throughout the world.
3. A paragraph needs to be added in introduction, stating the objective and major contribution
(in bullet points).
4. A section needs to be added for elaborating some prior state of work on this area of research.
5. The dataset section needs to be more elaborated, giving a detailed about the dataset that is
considered in this work.
6. Could you elaborate on why you chose these CNN models - DenseNet121, EfficientNetB2,
HRNet-W18, ResNet50, and MobileNetv3-large?
7. Do add detailed explanations of each performance metric, particularly the specific meaning
and application scenarios of accuracy, precision, recall, specificity, AUC and F1 score.
8. Figure 3 should be referred at the beginning of the methods section so that the reader can get
a pictorial idea about the whole work from initial phase.
9. The section heading of “Application Development” is repeated.
10. To enhance the quality of the paper, a critical discussion can be added mentioning the pros
and cons of the proposed work.
11. Go through the article thoroughly for grammatical and typological errors.
12. Re-write the conclusion so as to incorporate an overall outcome of the study, limitations
and future scope.
Please review this paper.
user
Title: Establishing an AI Model and Application for Automated Capsule Endoscopy
Recognition Based on Convolutional Neural Networks (with Video)
Version: 1 Date: 19 Sep 2024
Reviewer's report:
The work on automated capsule endoscopy recognition is highly impressive and demonstrates a
significant advancement in the field of small bowel lesion diagnostics. However, there are still
some issues in the article.
1. There is no numbering provided to the section and sub-sections, so that it will be more easy
to refer and understand.
2. A statistical graph or figure can be included in the introduction section to give a pictorial idea
about the present scenario of gastrointestinal diseases throughout the world.
3. A paragraph needs to be added in introduction, stating the objective and major contribution
(in bullet points).
4. A section needs to be added for elaborating some prior state of work on this area of research.
5. The dataset section needs to be more elaborated, giving a detailed about the dataset that is
considered in this work.
6. Could you elaborate on why you chose these CNN models - DenseNet121, EfficientNetB2,
HRNet-W18, ResNet50, and MobileNetv3-large?
7. Do add detailed explanations of each performance metric, particularly the specific meaning
and application scenarios of accuracy, precision, recall, specificity, AUC and F1 score.
8. Figure 3 should be referred at the beginning of the methods section so that the reader can get
a pictorial idea about the whole work from initial phase.
9. The section heading of “Application Development” is repeated.
10. To enhance the quality of the paper, a critical discussion can be added mentioning the pros
and cons of the proposed work.
11. Go through the article thoroughly for grammatical and typological errors.
12. Re-write the conclusion so as to incorporate an overall outcome of the study, limitations
and future scope.
assistant