Title: AI Support for Colonoscopy Quality Control Using CNN and Transformer Architectures
Version: 1 Date: 27 Jul 2024
Reviewer:  Ishak Paçal
Reviewer's report:
The study uses deep learning techniques for Colonoscopy Quality Control. The important points
that need to be addressed in the study are as follows:
- The abstract is too long and should be shortened.
- The introduction is too brief. It should start with information about CRC, including statistical
data on cases and mortality rates, its development, followed by an overview of colonoscopy,
deep learning, CAD diagnostic systems, and then details on CNN and ViT algorithms. Mention
existing models in the literature, research gaps, and the motivation behind the study. Support
the impact of deep learning on cancer diagnosis with state-of-the-art methods.
- [Reference 1](https://link.springer.com/article/10.1007/s13042-024-02110-w)
- [Reference 2](https://www.sciencedirect.com/science/article/pii/S0950705124001175)
- Add a new section on CRC, i.e., a literature review including SOTA methods.
- [Reference 3](https://www.sciencedirect.com/science/article/pii/S0957417423002427)
- [Reference 4](https://link.springer.com/article/10.1007/s10489-022-04299-1)
- [Reference 5](https://www.sciencedirect.com/science/article/pii/S0010482521008258)
- [Reference 6](https://www.sciencedirect.com/science/article/pii/S0010482521003139)
- In the image preprocessing section, include a table detailing the train-validation-test sets for
both datasets.
- Provide the basic mathematical formulas for CNN and ViT.
- Include a brief paragraph explaining each architecture.
- Clearly explain Grad-CAM and other XAI methods, including their mathematical backgrounds.
- Provide formulas for performance metrics.
- Correct the mention of "Efficient" to specify which EfficientNet model you used. There
is no "Efficient" model; there are EfficientNet models such as EfficientNetB0-B8 and
EfficientNetV2-S, -M, -L. Use all EfficientNet models.
- Consider including other SOTA methods like Swin, DeiT, and MobileViT. Use at least three
models for each architecture.
- Remove the metrics and graphs related to the train set.
- Only use tables and metrics related to the test set, not the validation set. Split your dataset into
train, validation, and test sets.
- The conclusion is too short.
- The discussion is inadequate.
- Specify limitations and future directions.
- Use higher-quality and clearer graphs and images.
- Review the entire study as there are significant errors present.
- Improving these aspects will enhance the quality of the study.
Title: AI Support for Colonoscopy Quality Control Using CNN and Transformer Architectures
Version: 1 Date: 27 Jul 2024
Reviewer:  Ishak Paçal
Reviewer's report:
The study uses deep learning techniques for Colonoscopy Quality Control. The important points
that need to be addressed in the study are as follows:
- The abstract is too long and should be shortened.
- The introduction is too brief. It should start with information about CRC, including statistical
data on cases and mortality rates, its development, followed by an overview of colonoscopy,
deep learning, CAD diagnostic systems, and then details on CNN and ViT algorithms. Mention
existing models in the literature, research gaps, and the motivation behind the study. Support
the impact of deep learning on cancer diagnosis with state-of-the-art methods.
- [Reference 1](https://link.springer.com/article/10.1007/s13042-024-02110-w)
- [Reference 2](https://www.sciencedirect.com/science/article/pii/S0950705124001175)
- Add a new section on CRC, i.e., a literature review including SOTA methods.
- [Reference 3](https://www.sciencedirect.com/science/article/pii/S0957417423002427)
- [Reference 4](https://link.springer.com/article/10.1007/s10489-022-04299-1)
- [Reference 5](https://www.sciencedirect.com/science/article/pii/S0010482521008258)
- [Reference 6](https://www.sciencedirect.com/science/article/pii/S0010482521003139)
- In the image preprocessing section, include a table detailing the train-validation-test sets for
both datasets.
- Provide the basic mathematical formulas for CNN and ViT.
- Include a brief paragraph explaining each architecture.
- Clearly explain Grad-CAM and other XAI methods, including their mathematical backgrounds.
- Provide formulas for performance metrics.
- Correct the mention of "Efficient" to specify which EfficientNet model you used. There
is no "Efficient" model; there are EfficientNet models such as EfficientNetB0-B8 and
EfficientNetV2-S, -M, -L. Use all EfficientNet models.
- Consider including other SOTA methods like Swin, DeiT, and MobileViT. Use at least three
models for each architecture.
- Remove the metrics and graphs related to the train set.
- Only use tables and metrics related to the test set, not the validation set. Split your dataset into
train, validation, and test sets.
- The conclusion is too short.
- The discussion is inadequate.
- Specify limitations and future directions.
- Use higher-quality and clearer graphs and images.
- Review the entire study as there are significant errors present.
- Improving these aspects will enhance the quality of the study.
Please review this paper.
user
Title: AI Support for Colonoscopy Quality Control Using CNN and Transformer Architectures
Version: 1 Date: 27 Jul 2024
Reviewer:  Ishak Paçal
Reviewer's report:
The study uses deep learning techniques for Colonoscopy Quality Control. The important points
that need to be addressed in the study are as follows:
- The abstract is too long and should be shortened.
- The introduction is too brief. It should start with information about CRC, including statistical
data on cases and mortality rates, its development, followed by an overview of colonoscopy,
deep learning, CAD diagnostic systems, and then details on CNN and ViT algorithms. Mention
existing models in the literature, research gaps, and the motivation behind the study. Support
the impact of deep learning on cancer diagnosis with state-of-the-art methods.
- [Reference 1](https://link.springer.com/article/10.1007/s13042-024-02110-w)
- [Reference 2](https://www.sciencedirect.com/science/article/pii/S0950705124001175)
- Add a new section on CRC, i.e., a literature review including SOTA methods.
- [Reference 3](https://www.sciencedirect.com/science/article/pii/S0957417423002427)
- [Reference 4](https://link.springer.com/article/10.1007/s10489-022-04299-1)
- [Reference 5](https://www.sciencedirect.com/science/article/pii/S0010482521008258)
- [Reference 6](https://www.sciencedirect.com/science/article/pii/S0010482521003139)
- In the image preprocessing section, include a table detailing the train-validation-test sets for
both datasets.
- Provide the basic mathematical formulas for CNN and ViT.
- Include a brief paragraph explaining each architecture.
- Clearly explain Grad-CAM and other XAI methods, including their mathematical backgrounds.
- Provide formulas for performance metrics.
- Correct the mention of "Efficient" to specify which EfficientNet model you used. There
is no "Efficient" model; there are EfficientNet models such as EfficientNetB0-B8 and
EfficientNetV2-S, -M, -L. Use all EfficientNet models.
- Consider including other SOTA methods like Swin, DeiT, and MobileViT. Use at least three
models for each architecture.
- Remove the metrics and graphs related to the train set.
- Only use tables and metrics related to the test set, not the validation set. Split your dataset into
train, validation, and test sets.
- The conclusion is too short.
- The discussion is inadequate.
- Specify limitations and future directions.
- Use higher-quality and clearer graphs and images.
- Review the entire study as there are significant errors present.
- Improving these aspects will enhance the quality of the study.
assistant