The Progress on Lung Computed Tomography Imaging Signs: A Review
Round 1
Reviewer 1 Report
The work is timely and comprehensive, however there is a real need to improve the English and cut redundancies.
There are many original work within the scope of this review that is not cited, given the major algorithms that are developed to detect Covid from other lung lesions such as cancer or ... Please provide a paragraph or a table to summarize them (e.g., Covid CT NET and/or others).
The conclusion part lacks clear referencing to various points that are made. The suggestions in that future direction are great and very impactful.
Author Response
Please see the attachment.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
1. Table 1, specify number of classes or class labels.
2. Why sign are important?
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Reviewer 3 Report
The author has provided a systematic summary and comparative analysis of the existing methods. they have shown the challenges ahead and discussed the directions for improvement of future work. This is relevant andinteresting to medical science. This paper is well-written and easy to read. The conclusions are consistent with the evidence and argumentspresented. I think, there is some type of error.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Reviewer 4 Report
The manuscript sounds technically poor, I have following concerns should be addressed before any decision.
*The existing literature should be classified and systematically reviewed, instead of being independently introduced one-by-one.
*The abstract is too general and not prepared objectively. It should briefly highlight the paper's novelty as what is the main problem, how has it been resolved and where the novelty lies?
*The 'conclusions' are a key component of the paper. It should complement the 'abstract' and normally used by experts to value the paper's engineering content. In general, it should sum up the most important outcomes of the paper. It should simply provide critical facts and figures achieved in this paper for supporting the claims.
*For better readability, the authors may expand the abbreviations at every first occurrence.
*The author should provide only relevant information related to this paper and reserve more space for the proposed framework.
*However, the author should compare the proposed algorithm with other recent works or provide a discussion. Otherwise, it's hard for the reader to identify the novelty and contribution of this work.
*The descriptions given in this proposed scheme are not sufficient that this manuscript only adopted a variety of existing methods to complete the experiment where there are no strong hypothesis and methodical theoretical arguments. Therefore, the reviewer considers that this paper needs more works.
*Key contribution and novelty has not been detailed in manuscript. Please include it in the introduction section
*What are the limitations of the related works
*Are there any limitations of this carried out study?
*How to select and optimize the user-defined parameters in the proposed model?
*There are quite a few abbreviations are used in the manuscript. It is suggested to use a table to host all the frequently used abbreviations with their descriptions to improve the readability
*Explain the evaluation metrics and justify why those evaluation metrics are used?
*Some sentences are too long to follow; it is suggested that to break them down into short but meaningful ones to make the manuscript readable.
*The title is pretty deceptive and does not address the problem completely.
*Every time a method/formula is used for something, it needs to be justified by either (a) prior work showing the superiority of this method, or (b) by your experiments showing its advantage over prior work methods - comparison is needed, or (c) formal proof of optimality. Please consider more prior works.
*The data is not described. Proper data description should contain the number of data items, number of parameters, distribution analysis of parameters, and of the target parameter itself for classification.
* The related works section is very short and no benefits from it. I suggest increasing the number of studies and add a new discussion there to show the advantage. Following studies can be added.
1. Lung nodules detection using semantic segmentation and classification with optimal features
2. Towards an effective model for lung disease classification: Using Dense Capsule Nets for early classification of lung diseases
3. A deep learning approach for COVID-19 8 viral pneumonia screening with X-ray images
*Use Anova test to record the significant difference between performance of the proposed and existing methods.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Round 2
Reviewer 4 Report
The paper is fine tuned and can be accepted.
