Kati8/ICLR_2023_2025 huggingface dataset:
authors: Agniva Banerjee
reviews: 
Comments: The author evaluates two CNNs on the task of renal disease classification. Two models are evaluated on one dataset.
Strengths: The paper presents the applied problem well.
Weaknesses: I am not sure what to think about this paper, because it has virtually no novelty and doesn't really seem like a research paper. There are no methodological contributions, and while the application may be interesting in its field, the experiment and the results are not extensive enough nor evaluated properly.

My major concerns with the paper have been stated already, but if a paper does not have a methodological contribution, I would expect it to have a scientific contribution somehow (in the application, for instance). Here, two existing neural networks are applied to an open dataset, and the highest mean performing model is dubbed the winner. All steps of the ML pipeline would have to be improved, basically. E.g., proper hyper-parameter search would be needed, more methods would have to be evaluated on more datasets, and confidence intervals, standard errors, or tests would have to be performed to say anything. But even with proper evaluation, this paper does not seem like a fit for ICLR, and should then rather be sent to a journal for renal disease studies.
Recommendation: 1
Reviewer confidence: 5
Soundness/correctness: 1
Clarity: 2
reviews: 
Comments: The authors used two advanced computer models called ResNet-50 and EfficientNetV2, which were already trained on large image databases, to analyze CT scans of kidneys. These models were tested on four different tasks: comparing healthy kidneys to those with cysts, cysts to stones, cysts to tumors, and stones to tumors. Both models performed very well, but EfficientNetV2 was slightly better, achieving perfect accuracy in almost all cases. The models were trained using special techniques to make them learn better and faster. The study found that these tools could be really useful in medical settings to help diagnose kidney diseases quickly and accurately. However, the authors note that future research should use larger and more diverse datasets, combine different types of medical images, and test the models in real-world hospital settings to make sure they work well for different patients.
Strengths: The study utilized multiple dataset.
Weaknesses: The novelty of this work is limited. Transfer learning is already studied in medical image analysis for long years. There is no novel method or contribution in this work. The evaluation of this work is insufficient. Comprehensive evaluation is required. The figures quality needs improve.
Recommendation: 3
Reviewer confidence: 5
Soundness/correctness: 1
Clarity: 2
reviews: 
Comments: In this study, the authors developed a deep learning (DL) framework that utilizes transfer learning (TL) for the early detection of renal diseases and the classification of conditions through the analysis of CT scans and microscopic histopathology images. The proposed framework employs convolutional neural networks (CNNs) with pre-trained models to enable automatic and accurate classification of renal conditions.
Strengths: The paper is well-organized and clearly written.

The proposed method is technically sound.

The collected dataset for developing and evaluating the proposed method is relatively large-scale.
Weaknesses: The novelty of this work is limited from a methodological perspective.

The interpretation of deep learning models is crucial in clinical practice; however, the authors have rarely presented such results.

Additionally, there is a lack of comprehensive comparisons with other studies.
Recommendation: 3
Reviewer confidence: 4
Soundness/correctness: 2
Clarity: 2