Title: A newly developed deep learning-based system for automatic detection and classification
of small bowel lesions during double-balloon enteroscopy examination
Version: 2 Date: 12 Jun 2023
Reviewer's report:
• There are a lot of scopes for authors to improve this work by doing model selection, model
tuning, dataset revision (doing split of train-test-validation data), training improvement (using
cross validation).
• Section 1.2: The authors did not mention clearly how many normal and abnormal images were
used for Model1 and Model2. It is better to provide a table for this.
• Section 1.2: The training images for the models seem imbalanced as the number of
abnormal samples (4201) are four times more than that of normal samples (1000). Class-wise
augmentation can be used to address imbalance.
• Section 1.2: The description of dataset is confusing. The author mentioned 915 abnormal
images and 1318 normal images are obtained from 187 patients. In the next sentence the author
mentioned 915 abnormal images were obtained from 98 patients. It is not clear those 915 samples
belong to same group of patients.
• Section 1.2: Both Model1 and Model2 are using same set of samples (915) for testing?
• Section 1.2: “Each video clip was cropped into consecutive images by 25 frames per second”
this line seems misleading. Cropping refers to resizing the video into different resolution/
duration. The authors can rewrite as: “Each video clip was used to extract consecutive images
at 25 frames per second rate”.
• Section 2: The authors should provide some references for YOLO and Resnet-50. This section
needs a lot more details.
• Section 3.1: The title for this section should be “Image Preprocessing”?
• Section 3.1: The images are annotated using a hybrid approach that includes both annotator
and human experts. It is not clear how the annotator results are validated.
• Section 4.1: The Assessment of YOLO can be explained in more detailed way.
• Section 4.2: Model2 uses a threshold to classify samples. It is better to share the threshold
values/ranges for better understanding.
• The English of this paper needs a significant improvement.
Title: A newly developed deep learning-based system for automatic detection and classification
of small bowel lesions during double-balloon enteroscopy examination
Version: 2 Date: 12 Jun 2023
Reviewer's report:
• There are a lot of scopes for authors to improve this work by doing model selection, model
tuning, dataset revision (doing split of train-test-validation data), training improvement (using
cross validation).
• Section 1.2: The authors did not mention clearly how many normal and abnormal images were
used for Model1 and Model2. It is better to provide a table for this.
• Section 1.2: The training images for the models seem imbalanced as the number of
abnormal samples (4201) are four times more than that of normal samples (1000). Class-wise
augmentation can be used to address imbalance.
• Section 1.2: The description of dataset is confusing. The author mentioned 915 abnormal
images and 1318 normal images are obtained from 187 patients. In the next sentence the author
mentioned 915 abnormal images were obtained from 98 patients. It is not clear those 915 samples
belong to same group of patients.
• Section 1.2: Both Model1 and Model2 are using same set of samples (915) for testing?
• Section 1.2: “Each video clip was cropped into consecutive images by 25 frames per second”
this line seems misleading. Cropping refers to resizing the video into different resolution/
duration. The authors can rewrite as: “Each video clip was used to extract consecutive images
at 25 frames per second rate”.
• Section 2: The authors should provide some references for YOLO and Resnet-50. This section
needs a lot more details.
• Section 3.1: The title for this section should be “Image Preprocessing”?
• Section 3.1: The images are annotated using a hybrid approach that includes both annotator
and human experts. It is not clear how the annotator results are validated.
• Section 4.1: The Assessment of YOLO can be explained in more detailed way.
• Section 4.2: Model2 uses a threshold to classify samples. It is better to share the threshold
values/ranges for better understanding.
• The English of this paper needs a significant improvement.
Please review this paper.
user
Title: A newly developed deep learning-based system for automatic detection and classification
of small bowel lesions during double-balloon enteroscopy examination
Version: 2 Date: 12 Jun 2023
Reviewer's report:
• There are a lot of scopes for authors to improve this work by doing model selection, model
tuning, dataset revision (doing split of train-test-validation data), training improvement (using
cross validation).
• Section 1.2: The authors did not mention clearly how many normal and abnormal images were
used for Model1 and Model2. It is better to provide a table for this.
• Section 1.2: The training images for the models seem imbalanced as the number of
abnormal samples (4201) are four times more than that of normal samples (1000). Class-wise
augmentation can be used to address imbalance.
• Section 1.2: The description of dataset is confusing. The author mentioned 915 abnormal
images and 1318 normal images are obtained from 187 patients. In the next sentence the author
mentioned 915 abnormal images were obtained from 98 patients. It is not clear those 915 samples
belong to same group of patients.
• Section 1.2: Both Model1 and Model2 are using same set of samples (915) for testing?
• Section 1.2: “Each video clip was cropped into consecutive images by 25 frames per second”
this line seems misleading. Cropping refers to resizing the video into different resolution/
duration. The authors can rewrite as: “Each video clip was used to extract consecutive images
at 25 frames per second rate”.
• Section 2: The authors should provide some references for YOLO and Resnet-50. This section
needs a lot more details.
• Section 3.1: The title for this section should be “Image Preprocessing”?
• Section 3.1: The images are annotated using a hybrid approach that includes both annotator
and human experts. It is not clear how the annotator results are validated.
• Section 4.1: The Assessment of YOLO can be explained in more detailed way.
• Section 4.2: Model2 uses a threshold to classify samples. It is better to share the threshold
values/ranges for better understanding.
• The English of this paper needs a significant improvement.
assistant