reviews: 
Comments: The author borrow on-shelf convolution neural network (ResNet50 and EfficientNet) to test on a kidney dataset (CT scan) and report the result by accuracy, precision, and recall.
Strengths: I understand that making the following judgment on a paper requires caution, which I will clarify later: there is no strength in this paper
Weaknesses: I find that this paper seems to have been generated by a scientific LLM agent. There are many inconsistencies in citations and formatting that appear to be hallucinations.


Here are just few examples that can be easily spot and verified:

Introduction section

- (In paper) Kidney cancer is a growing public health concern, affecting over 10% of the global population, and
projected to rise from the tenth leading cause of death in 2024  (Can, accessed 20th Sep, 2023) 
- (My verification) There is no mention of a '10% global population' or a 'projection to rise from the tenth leading cause' in the provided link. Additionally, the citation format does not appear to be human-written.
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-  (In paper) Kidney cysts are fluid-filled sacs, while nephrolithiasis affects approximately 17% of the global population (Alelign & Petros, 2018).
- (My verification) Not 17%, it is 12% in the provided link
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- (In paper) Computed tomography (CT) scans are particularly effective for kidney examinations, offering 3D (dimensional), cross-sectional images ideal for identifying abnormalities like cysts, stones, and tumors (Schmidt, 2012)."
- (My verification): The cited article does not provide this information or endorsement


Related work section

- (In paper) "CNN is a prominent DL algorithm for classifying grid-patterned datasets like images (Tan & Le,2020). "
- (My verification): Directly citing EfficientNet here feels out of place. In the community, it is more common to reference foundational papers like AlexNet, VGG, or those that pioneered CNN applications in the early stages of deep learning. Also, the phrase 'grid-patterned dataset' does not sound natural.
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- (In paper)Page 2 line 101-107: The statement "I.....  I..... I.....": 
- This resembles Claude Sonnet’s writing style when it assists in designing code or formulating ideas.


In 3.2 INITIALIZATION OF WEIGHTS
- (In paper) TL (Zhuang et al., 2020) is a powerful technique that significantly enhances training efficiency, especially when data is limited. It involves taking a pre-trained model, often trained on a large dataset like ImageNet, and fine-tuning it for a different but related task
- It’s quite odd to attribute transfer learning, a general machine learning technique, to a recent work from 2020. People in the community were familiar with this approach well before 2020, which makes it seem like an LLM hallucination.

In 3.3 TRAINING RESULT AND DL MODELS
- (In paper)  Two modern CNN architectures ResNet-50 and EfficientNetV2 are used, and both models were trained and evaluated using TensorFlow (Tan & Le, 2021; He et al., 2016; Khan et al., 2020)
- (My verification): This citation seems random; the correct citation for TensorFlow is: Abadi et al., 'TensorFlow: A System for Large-Scale Machine Learning.' Even if this citation were meant to credit EfficientNet or ResNet-50, the final reference to Khan et al., 2020 is irrelevant.
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- (In paper) Each of these blocks incorporates a skip connection, in addition to convolutional layers and batch normalization (Ioffe & Szegedy, 2015; Chen et al., 2020)
- (My verification): Ioffe & Szegedy, 2015 for  batch normalization is correct, but the later citation is SimCLR which is completely irrelevant

Hallucination aside, let me summarize the weakness of experiment design
- Reporting 100% accuracy without detailed analysis is suspicious, as this would imply a complete solution to the task—something that is extremely rare in the machine learning community. Even the latest models struggle to achieve 100% accuracy on datasets like CIFAR-10
https://paperswithcode.com/sota/image-classification-on-cifar-10
- It is unclear why the authors transformed a multi-class classification dataset into binary classes (e.g., cyst vs. normal, tumor vs. normal). There appears to be no benefit in deploying multiple binary classification models over the original multi-class approach
- Similar work was conducted in 2023, where ResNet50, EANet, and a proposed approach were compared on the same Kaggle dataset, rendering this paper's contributions negligible.
https://ieeexplore.ieee.org/document/10074314
Recommendation: 1
Reviewer confidence: 5
Soundness/correctness: 1
Clarity: 1