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The authors have addressed the reviewers' comments to satisfaction.
[# PeerJ Staff Note - this decision was reviewed and approved by Xiangjie Kong, a PeerJ Section Editor covering this Section #]
The authors shall address the reviewers' queries to satisfaction. In addition, the authors shall elaborate on the novelty of the proposal, the computational requirements and comparison to existing literature, and add statistical significance analysis to demonstrate that the improvements in performance are not by chance. The questions on the selection of feature extraction methods and methods of model optimization need to be properly addressed. The limitations of the proposal and scope for future work shall be discussed in detail.
No comment
No comment
1) Give a more detailed description for images in the dataset
2) Define each term in all equations
3) Add a detailed comparison (in tabulated form) with previous work in the field to show the significance of your results.
No additional comments
Paper is about feature extraction technique based on normalized group activations that can be applied to both structural MRI . An automated diagnosis system equipped with
the proposed feature extraction is designed and analyzed on multi-cohort Alzheimer's
Disease Neuroimaging Initiative(ADNI) data to predict multi-stages of AD. author should give more justification on the proposed feature extraction technique's. Till date many extraction techniques are available then how your is novel than the existing.
Include the column names in Table 5.
In Table 9: Comparison of recent methods, authors inserted results in bold text. Author should elaborate the importance for the same. some existing method (ResNet3-D) results are better than proposed method, if it is correct then justify proposed method novelty.
No comments
Paper sir well written. Author should give more focus on following points
1. Author should give more on novelty of the proposed features extraction model how it is better than FCNet(Riaz et al., 2020)
2. Alexnet and ResNet are normally extract features automatically then what is use of pass extracted features through this models. Give the proper justification.
The authors done a good job.
English is good
Given all the relevant information.
Abstract is good and it matches the conclusion.
Good
All the relevant information are give for this work
Given correct explanation for everything.
The way of writing is good.
Done enough survey and given in literature.
Good job.
The study introduces a unique feature extraction technique based on normalized group activations, which is applicable to both structural MRI and resting-state fMRI data.
The results provided should be substantiated with publicly available code. Provide github links for the code.
The proposed technique is applied to both rs-fMRI and MRI data; however, it briefly mentions the use of non-transformed features, curvelets, wavelets, and other techniques. It would be valuable to include a detailed comparative analysis of the proposed method against these established feature extraction methods to better quantify its advantages and limitations, and to provide insights into its relative strengths in different neuroimaging modalities. It would be better to conduct ablation study to understand fundamental benefits/limitations of these features.
The paper is well written however if it includes the following, it would further enhance the quality:
1) Providing publicly available code with appropriate comments for ease of checking by the research community.
2) Conducting ablation study.
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