اصلاح مقاله6

سلام وقت بخیر  Editor and Reviewer comments: Reviewer #1: 1. Innovative Approach: The integration of UNet++ with LSTM layers and self-attention mechanisms is commendable and addresses the temporal aspects often overlooked in image segmentation. 2. Performance Metrics: The reported accuracy (98.88%) and specificity (99.53%) are impressive. However, it would be beneficial to provide more context on how these metrics compare to existing state-of-the-art methods in detail. 3. Methodology Clarity: While the methodology is well-structured, a more detailed explanation of the Multiscale Feature Extraction Module would enhance understanding of its impact on segmentation performance. 4. Clinical Implications: The discussion on the clinical applicability of your findings is valuable. Expanding on potential limitations and future work could strengthen the manuscript further. 5. References: Ensure that all cited works are up-to-date and relevant to support your claims effectively. Overall, the manuscript is well-prepared and presents significant contributions to the field of medical imaging. Reviewer #2: Summary This paper presents a hybrid methodology combining UNet++ and LSTM layers for breast ultrasound image segmentation, addressing the critical issue of timely breast cancer detection. The authors claim significant improvements in segmentation accuracy and other metrics on the BUSI dataset. Major Comments 1.Introduction and Motivation (a) Clarity of Problem Statement: While the introduction outlines the importance of breast cancer detection, the specific challenges in existing segmentation methods could be articulated more clearly. A more detailed discussion of the limitations faced by UNet and UNet++ in capturing temporal features would strengthen the rationale for the proposed method. (b) Literature Review: The related works section is comprehensive but could benefit from a more critical analysis of the cited works. Highlighting the gaps in the current literature that this study aims to fill would enhance the context for your contributions. 2. Methodology: (a) Detailing of Proposed Model: The description of the hybrid model is somewhat high-level. More detailed explanations of the architecture, particularly the integration of LSTM and attention mechanisms with UNet++, are needed. Diagrams illustrating the model architecture would aid in comprehension. (b) Multiscale Feature Extraction Module: This component is introduced but not sufficiently elaborated upon. A clearer explanation of how this module functions and its impact on performance should be included. 3. Results and Discussion: (a) Comparative Analysis: While the authors present results that show improved metrics, a deeper discussion comparing the proposed method with specific baseline models is necessary. Highlighting which aspects of the proposed model led to these improvements would provide more insight. (b) Statistical Validation: The authors should include statistical analyses (e.g., p-values) to validate the significance of their results compared to existing methods. 4. Conclusion: (a) Implications for Clinical Practice: The conclusion briefly mentions clinical implications, but this could be expanded. Discussing how the proposed model could be integrated into clinical workflows or its potential impact on patient outcomes would provide added value. (b) Future Work: Suggestions for future research directions should be more explicitly stated. Identifying potential improvements or additional datasets for validation would be beneficial. 5. Formatting and Style: (a) Language and Grammar: The manuscript contains several grammatical errors and awkward phrasings that detract from clarity. A thorough proofreading is recommended to enhance readability. (b) Figures and Tables: Ensure that all figures and tables are clearly labeled and referenced in the text. Some figures may require more descriptive captions for better understanding. Note: Given the potential significance of this research in the field of medical imaging, I recommend a major revision of the manuscript. The authors should address the comments outlined above, particularly focusing on clarifying the methodology and enhancing the discussion around the results. The proposed approach shows promise, but clarity and depth in presentation are essential for its acceptance. I look forward to reviewing the revised manuscript. Reviewer #3: I have attached my recommendations for your attention. Reviewer #4: Given the following significant shortcomings, I recommend rejecting this paper and suggesting a substantial revision and improvement before considering it for resubmission. Similarity with other publication is significant. For instance it is 7% similar to this paper: https://doi.org/10.3390/app******* and has 19% overall similarity to other papers. (Checking with iThenticate) Reject: \* Lack of novelty: Although combining existing techniques (UNet++, LSTM, and attention mechanisms) can be considered as novelty, integration of these components is not justified thoroughly. \* Methodology: clear explanation of how LSTM layers integrated into Unet++ is lacking. (lack of reproducibility) \* Missing Ablation Study \* Poor writing quality. There are several grammatical and typos. o Table formatting is inconsistent. o Referencing is inconsistent. o Formulas have errors. Reviewer #5: Combining UNet++ with LSTM to capture temporal features and attention mechanism (CBAM) and multi-scale feature extraction module is interesting. However, the following comments need to be responded by the author to improve the quality of the paper, namely: 1. Explain why UNet++ is chosen over standard UNet or other models such as DeepLabV3, SegNet, paper *******/faith.2024-10 may be added as a reference for the reason for the selection. Transformer-based models such as Swin-Unet or TransUNet often show superior performance. 2. Explain further why LSTM was chosen over alternatives such as custom RNN, GRU or Bidirectional Techniques 3. Conduct ablation studies to evaluate the contribution of each added component (UNet++ alone, UNet++ with LSTM, UNet++ with CBAM, etc.) and explain the impact of each component on metrics such as sensitivity, specificity, and Dice coefficient. 4. Explain the rationale for choosing the BUSI dataset (connect it to the characteristics of the dataset). Why not use multiple datasets such as OASBUD or UDIAT, to demonstrate the generalization ability of the model. 5. Discuss how the model can handle noise or artifacts in real clinical data, as BUSI may not represent all real conditions. 6. Add ROC-AUC curves and sensitivity-specificity trade-off analysis to illustrate the model performance at different thresholds. 6. Discuss the clinical implications of false negatives and false positives, including their impact on patients. 7. Revisit the dropout rate (0.5), which may be too high for medical segmentation. A dropout rate of 0.5 may be too high because the model may lose too much important information. Experiments with lower dropouts (e.g., 0.2-0.3) may provide better stability. 8. Provide empirical evidence that dense skip connections in UNet++ provide significant improvements over methods without this feature.  هزینه ی این اصلاحات فقط موارد  اصلاحات نگارشی اش چه قدر میشه ؟ فقط موارد نگارشی رو می‌خوام موارد فنی رو نمی‌خوام