Vishesh Narendra Pamadi Reviewer
Approved
Relevance and Originality
This work contributes to the growing body of research on AI driven diagnostic systems. The focus on lightweight architecture is particularly valuable for real world deployment. While the integration of multiple techniques is commendable, the conceptual novelty is somewhat incremental rather than transformative.
Methodology
The methodological design is comprehensive and incorporates several advanced techniques. The dual branch architecture combining spatial and texture features is well justified. However, the manuscript would benefit from a clearer explanation of model training procedures, including validation strategy, data augmentation, and parameter selection. These omissions affect the transparency of the study.
Validity and Reliability
The reported evaluation metrics indicate strong model performance. The ablation study is a positive aspect, showing the contribution of each module. Still, the study does not address potential biases in the dataset or class imbalance issues in detail. External validation or comparison on independent datasets would improve reliability.
Clarity and Structure
The paper is generally understandable, but language quality requires improvement. Several sentences are lengthy and could be simplified. Minor inconsistencies in terminology and notation are also present. Improved editing would enhance readability and professionalism.
Results and Analysis
The results are clearly presented with supporting visualizations. The comparison with baseline models strengthens the claims. However, the analysis remains largely performance driven and lacks interpretability discussion, which is crucial in medical applications.
Vishesh Narendra Pamadi Reviewer