Advancing Sustainable COVID-19 Diagnosis: Integrating Artificial Intelligence with Bioinformatics in Chest X-ray Analysis
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThe study presents a method for analyzing chest X-ray images during the COVID-19 crisis, focusing on efficiency. By combining Neural Architecture Search with Genetic Algorithms, the approach refines Convolutional Neural Networks to reduce computational demands. Leveraging Transfer Learning to overcome data scarcity, the method optimizes time and hardware usage, supporting sustainable AI initiatives. Comparative analyses demonstrate improved performance and reduced ecological impact, marking a significant advancement in eco-sustainable medical imaging. The paper is well-written, with a comprehensive discussion of the results and a clear analysis. I commend the authors for producing a high-quality paper. However, I would like to offer a few constructive comments:
1. It would be valuable if the authors could explore the implementation of the proposed method on an additional dataset, allowing for a comparison of its performance against state-of-the-art systems.
2. Please provide a detailed explanation of the network parameters and how optimization improved the results.
3. Further clarification on why the proposed network outperforms other common deep neural networks would be beneficial. Identifying the specific components of the network that significantly impact the results would enhance the understanding of its superiority.
4. Incorporating methods to explain the proposed network, such as interpretability techniques, could enhance the paper. These techniques can translate the network's behavior into interpretable output, enabling a more insightful analysis of the predictions and answering questions about the network's performance.
5- Enhancements to the quality of the figures are warranted, particularly ensuring that the text within the figures is legible. Moreover, augmenting the information provided in the captions of the figures and tables would enhance their utility and comprehensibility.
Author Response
Special thanks to the anonymous referee for the time and effort spent to evaluate the article. In the revised version, the modifications suggested by reviewers are highlighted in blue.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsThis work presents and review/overview of integration of AI with Bioinformatics in Chest X-Ray Analysis for COVID Diagnostics. Major parts of this paper is written as a review paper - offer including comprehensive analysis of current research works. My recommendation is to completely convert this paper into a review article. In my opinion such a review is very valuable to the research community. In addition this field has been extensively studied by the authors in previous research - e.g.:
1. Louati, H., Louati, A., Bechikh, S., Masmoudi, F., Aldaej, A. and Kariri, E., 2022. Topology optimization search of deep convolution neural networks for CT and X-ray image classification. BMC Medical Imaging, 22(1), p.120.
2.Louati, H., Bechikh, S., Louati, A., Aldaej, A. and Said, L.B., 2022, July. Evolutionary optimization for CNN compression using thoracic X-ray image classification. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (pp. 112-123). Cham: Springer International Publishing.
3. Louati, A., Louati, H., Kariri, E., Alaskar, F. and Alotaibi, A., 2023. Sentiment Analysis of Arabic Course Reviews of a Saudi University Using Support Vector Machine. Applied Sciences, 13(23), p.12539.
4. Louati, H., Bechikh, S., Louati, A., Hung, C.C. and Said, L.B., 2021. Deep convolutional neural network architecture design as a bi-level optimization problem. Neurocomputing, 439, pp.44-62.
The authors could re-name the section headings and perhaps move some data/results to the appendix section.
Author Response
Special thanks to the anonymous referee for the time and effort spent to evaluate the article. In the revised version, the modifications suggested by the reviewer are highlighted in blue.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for Authors1.
Figure 3 shows normal and infected X-ray images. Please explain clearly which ones are normal X-ray images and which ones are infected X-ray images.
2.
Please label Figure 4 clearly.
3.
Please label Figure 5 clearly.
4.
Figure 6 shows the evaluation of standard and COVID-19 cases through sample analysis. Please explain clearly the labels in Figure 6.
5.
Please explain clearly TRP.TNR in Formula 2.
6.
Please explain Figure 7 clearly.
Author Response
Special thanks to the anonymous referee for the time and effort spent to evaluate the article. In the revised version, the modifications suggested by the reviewer are highlighted in blue.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsAll concerns have been addressed effectively by the revisions, clarifying ambiguous sections and improving the overall quality of the paper.
Reviewer 3 Report
Comments and Suggestions for AuthorsAccept in present form.
