All reviews of published articles are made public. This includes manuscript files, peer review comments, author rebuttals and revised materials. Note: This was optional for articles submitted before 13 February 2023.
Peer reviewers are encouraged (but not required) to provide their names to the authors when submitting their peer review. If they agree to provide their name, then their personal profile page will reflect a public acknowledgment that they performed a review (even if the article is rejected). If the article is accepted, then reviewers who provided their name will be associated with the article itself.
Thank you for your revised updated version, I'm pleased to inform you that experts are now happy with the revised updated paper. Therefore we recommend it for publication.
[# PeerJ Staff Note - this decision was reviewed and approved by Xiangjie Kong, a PeerJ Section Editor covering this Section #]
No comment
No comment
No comment
Thank you for submitting your revised manuscript. I have now had a chance to read it through, along with your responses to the points that I raised in my original review.
I am very happy with the way that you have addressed all of my comments and suggestions. The manuscript is now much improved and I am happy to recommend it for publication in its current form.
Thank you for submitting your revised manuscript. I have now had a chance to read it through, along with your responses to the points that I raised in my original review.
I am very happy with the way that you have addressed all of my comments and suggestions. The manuscript is now much improved and I am happy to recommend it for publication in its current form.
Thank you for submitting your revised manuscript. I have now had a chance to read it through, along with your responses to the points that I raised in my original review.
I am very happy with the way that you have addressed all of my comments and suggestions. The manuscript is now much improved and I am happy to recommend it for publication in its current form.
The experts have now commented on the paper's quality and you will see a couple of comments to be incorporated before we re-consider it. please carefully incorporate and consider the following points as well.
The paper does not provide sufficient information about the dataset used, such as the number of images, diversity of the dataset
While the DenseNet201 architecture was selected from eight different pre-trained models, the paper lacks a comprehensive comparison of these models' performances.
Although six classifiers were used, the paper only reports the accuracy of the SVM classifier. Providing performance metrics (e.g., precision, recall, F1-score) for all classifiers would give a more comprehensive evaluation of the system's performance and robustness.
Whats the logic behind using the Gaussian filter for denoising is mentioned.
I have examined the study you have carried out to detect knee osteoarthritis in detail. First, I would like to state that the focused problem is important. Original methodology is followed and promising results are presented. Many points for this problem have been successfully addressed, however there are some deficiencies.
A high performance is mentioned in the contribution and novelty section, but numerical values do not support this statement. When the proposed model section is examined, feature selection is performed with DenseNet201. It should be explained why these architectures were chosen although there are different architectures in the literature.
In Line 267, it is mentioned that the NCA method increases the model’s performance, but the results obtained without applying NCA in the study are not presented. How did you conclude?
When Table 1 is examined, it is seen that more than one pre-trained model was tested. However, the feature maps obtained in these models were not classified in the classifiers. Only the performances of the Densenet201 architecture on the classifiers are discussed. Adding other results will reveal the power of the proposed model more clearly.
You should express more clearly the factors or reasons that brought the model suggested in Table 6 to the fore. The statement that the experiments were carried out under the same conditions should be written. Limitations and suggestions should be included in the Discussion section. Future research directions should also be mentioned.
The expression “Knee osteoarthritis” has not been used in different parts of the article, and uppercase and lowercase letters have not been considered—for example, in line 22. Osteoarthritis (OA) was abbreviated in Line 18 and continued to be abbreviated in the rest of the article, for example, in Line 34. The abbreviations OA and KOA have been used interchangeably. This error should be reviewed.
The architecture names should be written in the same format throughout the article. Personal pronouns should not be used as much as possible. “Metics” in Table 6 should be corrected as “Metrics”. The spelling errors in Line 163 should be corrected. Reference used as “[h11]” in Line 267 must be corrected. All references should also be checked after this correction. Subtitle “Results obtained on pre-trained Models” should be corrected as “Results Obtained on Pre-trained Models”
I believe this well-written work has the potential to add to the body of knowledge on knee arthritis detection. However, the quality of the article will improve if some of the study's shortcomings are fixed.
1. The Abstract section should provide a more thorough explanation of the significance of automatic knee arthritis detection, the article's novel features, and its contributions to the literature.
2. The key results that were achieved should also be reported at the conclusion of the Abstract section.
3. The title makes reference to the connection between physical therapy and knee arthritis. In the contributions section, this statement must be emphasized.
4. The literature contains a few studies on this specific topic. It is necessary to expand these investigations. The literature review should also include a detailed discussion of the benefits and drawbacks of the pertinent studies.
5. Line 162 states that the suggested model produced competitive outcomes. It is also necessary to add the outcomes that were acquired here.
6. It is noted that the photos' noise was removed using the Gauss method. Nevertheless, the method section does not describe the Gauss method.
7. Please be very mindful of abbreviations. Explain acronyms when they are first used, using parenthesis to show the abbreviation. The abbreviation should then be used. Examine the use of OA for osteoarthritis, for instance. What is "NCA" in its extended form? In the manuscript, it is utilized straight without a lengthy version.
8. "Matlab" has to be changed to "MATLAB."
9. The full names of the writers should be added to Reference 1.
10. A single style should be used to write all the values in the tables. Values, for instance, can be expressed as two numbers following a comma.
11 Table 6 needs to be written accurately. For many publications, the "year" information in the second columns appears to be missing.
N/A
It is evident that the application results employ six distinct classifiers, and the outcomes of these classifiers are displayed. The method part doesn't go into detail about the classifiers.
The essay should include specifics on the Grad-cam technique. If possible, equations should be used to support the flaws listed.
The application result section provides a detailed presentation of the suggested model's outcomes. Nevertheless, the study's findings on the classifiers are only based on the Densenet201 design, ignoring other models.
A thorough discussion of the study's shortcomings is required.
All text and materials provided via this peer-review history page are made available under a Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.