AI-Driven Solutions for Falcon Disease Classification: Concatenated ConvNeXt cum EfficientNet AI Model Approach

Alavikunhu Panthakkan; Fatma Taher; Hussain Al-Ahmad; S M Anzar; Zubair Medammal

REVIEW 4 major objections 6 minor 9 references

AI-Driven Solutions for Falcon Disease Classification: Concatenated ConvNeXt cum EfficientNet AI Model Approach

T0 review · 4 major / 6 minor · reviewed 2026-08-08 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims that a concatenation of ConvNeXt and EfficientNet feature representations classifies falcon endoscopy images into Normal, Liver, and Aspergillosis with 98% average test accuracy, beating both standalone backbones.

desk verdict The 98% claim rests on an unseeded image-level split of 610 images with no bird-level separation; the approach is standard, the evaluation is not yet rigorous enough to support the claim. read the letter →

arxiv 2502.04682 v1 pith:4ZXGQBQX submitted 2025-02-07 cs.CV

Alavikunhu Panthakkan , Zubair Medammal , S M Anzar , Fatma Taher , Hussain Al-Ahmad This is my paper · ORCID

classification cs.CV

keywords falcondiseaseclassificationConvNeXtEfficientNetconcatenatedmodeltransferlearningendoscopyimagesavianveterinaryAIaspergillosisdetection

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that joining two pretrained convolutional networks, ConvNeXt and EfficientNet, into one model accurately classifies falcon endoscopy images into three health categories: normal, liver disease, and aspergillosis. On a dataset of 610 images, the concatenated model reaches 98% average test accuracy, roughly 11 points above ConvNeXt alone and 4 points above EfficientNet alone. The authors argue that the two architectures extract complementary features, so combining them captures a wider range of diagnostic cues than either network on its own. If the result holds, it offers a practical recipe for veterinary disease screening from endoscopic images with a relatively small labeled dataset.

What carries the argument

The central object is the concatenated AI model: the feature representations produced by ConvNeXt, a modern convolutional backbone built around depthwise-separable convolutions, and EfficientNet, a backbone whose compound scaling balances depth, width, and resolution, are joined before a final three-class classifier. The input layer is fixed to 128 by 128 endoscopy images, and transfer learning initializes both branches from pretrained weights before fine-tuning on the falcon dataset. The concatenation step is what carries the argument: it allows the classifier to see both backbones' representations at once, and the paper attributes the accuracy gain to this combined feature set.

What would settle it

Count, for each individual falcon in the collected endoscopy images, how many images contribute to the training set and how many to the test set. If any bird contributes images to both sides, retrain the same model with a bird-exclusive split; if the test accuracy falls materially below 98%, the reported superiority of the concatenated model is an artifact of image-level leakage rather than a genuine diagnostic gain.

Watch

Extended reading notes

Core claim

The central claim is that a model formed by concatenating the feature representations of ConvNeXt and EfficientNet classifies falcon endoscopy images into Normal, Liver, and Aspergillosis with 98% average test accuracy, surpassing the 87% of ConvNeXt and 94% of EfficientNet. The paper reports per-class F1 scores of 0.96, 0.99, and 0.98 for the three classes, with a training accuracy of 99.65% and a validation accuracy of 98.50% over 50 epochs. The authors interpret the result as evidence that the two backbones capture complementary image features, and that the concatenated model is more reliable for this diagnostic task than either model alone.

Load-bearing premise

The load-bearing premise is that the single random 80/20 split of 610 images gives an honest test set, with no images of the same falcon appearing in both training and test; the paper does not report bird-level separation or cross-validation.

Editorial extensions

If this is right

On the reported dataset, the concatenated model attains 98% average test accuracy, so a veterinarian screening endoscopy images could expect roughly 98 of every 100 images to be labeled correctly.
The hybrid beats both standalone backbones by a clear margin, 98% versus 87% and 94% average accuracy, so combining features is more useful on this task than either architecture alone.
Per-class results, with F1 scores of 0.96 for normal, 0.99 for liver, and 0.98 for aspergillosis, imply the model is not simply memorizing the majority class and does separate the two disease classes from normal tissue and from each other.
Since training used 80% of only 610 images with transfer learning, the result suggests pretrained backbones can be adapted to a niche veterinary domain with a few hundred labeled examples.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the accuracy gain is real, the same concatenation recipe is a cheap thing to try on any small medical-imaging dataset with two pretrained convolutional backbones; the paper's numbers suggest the benefit may transfer without task-specific tuning.
The natural stress test is a bird-exclusive split: the authors could report performance when no individual falcon contributes images to both training and test, which would separate genuine generalization from image-level memorization.
Because the paper compares only against its own two backbone models on one split, it leaves open whether the 98% figure would survive comparison with published bird-disease classifiers or with a single-backbone model trained with the same data augmentation and tuning.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Short take: this is a thin empirical paper whose central accuracy claim is not backed by the evaluation design. The core problem is the split.

What's actually here: a real veterinary problem, a genuine dataset from Sharjah Falcon Clinic, and a straightforward head-to-head comparison of a feature-concatenated ConvNeXt+EfficientNet against both backbones. That is worth something. The authors do not falsify numbers; the reported metrics are internally consistent, and the method is a reasonable thing to try.

But the evidence is too weak to carry the "superior performance" conclusion. The dataset is 610 endoscopy images, split 80/20 at the image level, and the paper never says how many individual falcons contributed images. If frames from the same bird appear in both training and test, the model can memorize per-bird artifacts (illumination, scope angle, lesion texture) and the 98% figure is inflated. The text also calls the 20% split both "internal validation" and "test set" and says hyperparameter tuning was done on the validation set, so the final test numbers may not be independent. There are no repeated splits, no confidence intervals, no external validation. The literature review is weak, and reference [2] is a duplicate of [1]. Section IV contains a repeated paragraph.

None of this means the method is nonsense. It is a standard transfer-learning pipeline with a generic feature-concatenation trick. The flaws are fixable: report the number of birds, do a falcon-level split, run multiple seeds, and show error bars. If the authors can do that, there is a usable veterinary screening tool hiding here. As it stands, the paper is not publishable.

My recommendation: send it to peer review only if the venue demands those revisions. The current submission would not survive, but the underlying experiment is real and the topic is underserved. I would not cite it in its current form, and I would not bring it to reading group except as an example of image-level leakage in medical imaging. If you do review it, ask for bird-level cross-validation and insist on code and data release.

Referee Report

4 major / 6 minor

Summary. The manuscript proposes a hybrid deep learning model that concatenates feature representations from ConvNeXt and EfficientNet to classify falcon endoscopic images into three classes ('Normal', 'Liver', 'Aspergillosis'). The authors use 610 images from a falcon clinic, with an 80/20 train/test split, and report that the concatenated model reaches about 98% test accuracy, compared to 87% for ConvNeXt and 94% for EfficientNet. The paper includes confusion matrices, a ROC curve, and per-class precision/recall/F1 metrics. The central claim is that the concatenated model is superior to the standalone architectures and to 'traditional methods', though no non-deep baseline is actually evaluated.

Significance. If the reported accuracy were robust, the work would provide a practical baseline for AI-assisted avian endoscopy, and the concatenation approach is simple enough to be adopted easily. The paper has some virtues: it uses real clinical images, reports class-specific metrics, and gives a pseudocode-level description of the pipeline. However, the evidence as presented is not yet sufficient to establish the superiority claim. The lack of falcon-level separation in the random split, the conflation of validation and test sets, and the absence of any variance estimate are serious threats to the validity of the headline number. These are standard and fixable evaluation issues, but they must be addressed before the claim can be accepted.

major comments (4)

[Section IV, dataset split] The paper states that the dataset was 'randomly divided' into 80% training and 20% test, but it does not report the number of individual falcons or whether images from the same falcon can appear in both partitions. Endoscopy acquisitions commonly contain multiple frames per bird; if the split is image-level, the 98% test accuracy may be inflated by bird-specific or session-specific artifacts. Please report the falcon-level distribution and repeat the evaluation with a split that groups all frames from each falcon into one partition, e.g., leave-one-falcon-out cross-validation.
[Section IV, validation/test terminology] The text says the remaining 20% was used for 'internal validation' and that 'hyperparameter fine-tuning was executed using the validation set,' yet it also refers to the same portion as the 'test set' and reports '126 test images' in the confusion matrix. If hyperparameters were chosen on the test set, the reported accuracy is an optimistic selection result, not an independent test statistic. Please clarify whether the reported numbers come from a fully held-out test set and, if not, provide an independent test evaluation.
[Table I and Section V, statistical support] The superiority claim rests on a single 80/20 split with no repeated runs, confidence intervals, or cross-validation. With only 126 test images, the difference between the reported 98% and 94% accuracies corresponds to a handful of examples, and without variance estimates one cannot judge whether it is significant. Please report per-class confidence intervals and results over multiple seeds or k-fold cross-validation.
[Section III and Section IV, architecture and training details] The proposed 'concatenated' model is described only by the pseudocode bullet 'construct the concatenated deep learning model incorporating features from ConvNeXt and EfficientNet.' The fusion layer design, feature dimensions, optimizer, learning rate, data augmentation, and class-balance handling are not reported. Without these details, the 98% result cannot be reproduced or attributed to the concatenation rather than to unspecified training choices. Please provide a precise architecture description and a complete training protocol.

minor comments (6)

[References] References [1] and [2] are identical; please replace the duplicate with the actual reference for the deep-learning falcon-disease paper discussed in Section II.
[Section IV] Section IV contains the same paragraph twice, starting with 'To evaluate the model's performance...' – please remove the duplicate.
[Section IV] Section IV refers to 'Figure 7x' for the dataset distribution; the correct figure is Fig. 3.
[Abstract and Conclusion] The abstract and conclusion claim that the concatenated model outperforms 'traditional methods', but Table I only compares against the two standalone CNN models; please either add a traditional baseline or soften the claim.
[Section II, ConvNeXt description] The description of ConvNeXt as 'developed by Google Research' is inaccurate; it should be credited to Liu et al. (Meta AI) and cited properly.
[Figure 2] Figure 2 shows the concatenated architecture but the text never explains the fusion layer or the dimensions of the concatenated feature vector; please add an explicit description.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the 98% test accuracy is an empirical held-out evaluation, not a derivation from its inputs.

full rationale

The paper's central claim is empirical: the concatenated ConvNeXt-EfficientNet model achieves 98% accuracy on a 126-image test set (Section IV, Table I, and Section V). This result is produced by training on an 80% split, tuning hyperparameters on a validation subset, and then evaluating on the remaining test images. No equation in the paper defines the reported accuracy in terms of the fitted parameters, and no parameter is fitted to the test labels. There is no self-citation chain, no imported uniqueness theorem, and no ansatz smuggled in via citation; the proposed model is described as a direct concatenation of two standard published architectures, and the comparison is against those same architectures on the same data. The paper's weaknesses, such as the unseeded image-level 80/20 split, the unreported number of individual falcons, the possibility of frame-level leakage across splits, and the ambiguous use of 'internal validation' versus 'test set,' are threats to external validity and could inflate the reported accuracy, but they are data-construction and evaluation-protocol concerns rather than circularity. The test metric is not equivalent by construction to the training objective, to a fitted parameter renamed as a prediction, or to any input definition. The duplicated reference [1]/[2] and the repeated paragraph in Section IV are presentation defects with no load-bearing role in the argument. Therefore no significant circularity is found.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The central accuracy claim depends on a small private dataset, an unverified single split, incomplete training details, and the untested assumption that feature concatenation adds information. No new entities are introduced, and the only fitted quantities are the network weights and hyperparameters listed above.

free parameters (6)

Input image resolution = 128 x 128
Chosen in the pseudocode (Section III); no ablation study shows this resolution is adequate for endoscopy detail.
Batch size = 5
Set in Section IV; no sensitivity analysis or rationale given.
Training epochs = 50
Set in Section IV; no early stopping, learning rate schedule, or convergence analysis reported.
Train/validation split = 80/20 random
Section IV; single split without seed or cross-validation; test set is only 126 images.
Feature fusion design = not specified
The paper describes the model as 'concatenated' but does not specify which layers are concatenated or how the merged features are classified (Section III).
Optimizer and learning rate = not reported
Training details are incomplete; no optimizer, learning rate, weight decay, or augmentation is stated (Section IV).

assumptions (5)

domain assumption ImageNet-pretrained features transfer to falcon endoscopy images.
The method relies on transfer learning (Section III) but gives no evidence that features from natural images generalize to endoscopic images; no fine-tuning or layer-freezing analysis is presented.
domain assumption The 610 images are independent and the random split prevents leakage.
Section IV splits the dataset randomly, but the paper does not state how many birds contributed images or whether multiple frames from one bird appear in both training and test. Leakage would inflate the reported accuracy.
domain assumption Clinical labels Normal, Liver, and Aspergillosis are correct ground truth.
Labels come from Sharjah Falcon Clinic and are taken as ground truth; no veterinary verification protocol, inter-rater agreement, or diagnostic confirmation is described.
ad hoc to paper Concatenating ConvNeXt and EfficientNet features improves discriminative information.
This is the core architectural premise. The paper provides no theoretical or empirical evidence that simple concatenation is superior to either network or to other fusion methods; it is assumed by construction.
standard math Standard stochastic optimization converges as expected.
The paper assumes backpropagation and the chosen optimizer train the model reliably; no repeated runs with different seeds are reported, so the optimization outcome is not characterized.

how reviews work

0 comments

Cite this review

Pith. "Pith review of AI-Driven Solutions for Falcon Disease Classification: Concatenated ConvNeXt cum EfficientNet AI Model Approach." pith.science (2026). https://pith.science/paper/4ZXGQBQX

@misc{pith2026250204682,
  author       = {Pith},
  title        = {Pith review of: AI-Driven Solutions for Falcon Disease Classification: Concatenated ConvNeXt cum EfficientNet AI Model Approach},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4ZXGQBQX}},
  note         = {Machine review of arXiv:2502.04682}
}

read the original abstract

Falconry, an ancient practice of training and hunting with falcons, emphasizes the need for vigilant health monitoring to ensure the well-being of these highly valued birds, especially during hunting activities. This research paper introduces a cutting-edge approach, which leverages the power of Concatenated ConvNeXt and EfficientNet AI models for falcon disease classification. Focused on distinguishing 'Normal,' 'Liver,' and 'Aspergillosis' cases, the study employs a comprehensive dataset for model training and evaluation, utilizing metrics such as accuracy, precision, recall, and f1-score. Through rigorous experimentation and evaluation, we demonstrate the superior performance of the concatenated AI model compared to traditional methods and standalone architectures. This novel approach contributes to accurate falcon disease classification, laying the groundwork for further advancements in avian veterinary AI applications.

Figures

Figures reproduced from arXiv: 2502.04682 by the authors.

**Figure 1.** UAE Falcon B. Falcon Diseases Falcon diseases encompass a range of health issues that can impact the well-being of these birds of prey. One common ailment is 'Liver' disease, often attributed to nutritional imbalances or infectious agents. Another notable disease is 'Aspergillosis' a fungal infection caused by the Aspergillus [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

9 extracted references · 9 canonical work pages

[2]

retrospective study on the health problems of falcons in Al Ain, United Arab Emirates

Al Hemeiri, M. A, Mohteshamuddin, K, de la Torre, A. A, Degafa, B. A and Ameni, G. A, “retrospective study on the health problems of falcons in Al Ain, United Arab Emirates”, Emirates Journal of Food and Agriculture, pp. 51-58, 2022

work page 2022
[3]

Bird Disease Classification Using Pre - trained Convolutional Neural Networks

Yildiz, B and Aslan, M. M, “Bird Disease Classification Using Pre - trained Convolutional Neural Networks” in Applied Artificial Intelligence, pp. 1429-1448, 2022

work page 2022
[4]

A Hybrid Deep Learning Approach for Automatic Bird Species Recogni tion from Images

Zhang Y, Liu S, Zhang W and Guo J, “A Hybrid Deep Learning Approach for Automatic Bird Species Recogni tion from Images” in Multimedia Tools and Applications, Journal, pp. 17083-17099

work page
[5]

Transfer Learning for Bird Species Classification with Deep Convolutional Neural Networks

Van Beijl L and Moons T, “Transfer Learning for Bird Species Classification with Deep Convolutional Neural Networks” in Sensors, Journal, 21(13), 4405

work page
[6]

Bird Disease Detection Using Deep Learning Techniques

A. Al-Hamad, A. A. Abouseif, A. A. Salem, and M. I. El-Hakeem, “Bird Disease Detection Using Deep Learning Techniques” in International Journal of Advanced Computer Science and Applications, 13(2), pp. 334- 343

work page
[7]

M. S. Ali, M. S. Miah, J. Haque, M. M. Rahman, M. K. Islam, An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models, Machine Learning with Applications 5 (2021) 100036

work page 2021
[8]

S. S. Chaturvedi, J. V. Tembhurne, T. Diwan, A multi -class skin cancer classification using deep convolutional neural networks, Multimedia Tools and Applications 79 (2020) pp. 28477–28498

work page 2020
[9]

V. Balasubramaniam, Artificial intelligence algorithm with svm classification using dermascopic images for melanoma diagnosis, Journal of Artificial Intelligence and Capsule Networks 3 (2021) pp.34–42

work page 2021

Show all 9 references

[10]

Development of deep learning model for prediction of chemotherapy response using PET images and radiomics features,

W. Kim et al., "Development of deep learning model for prediction of chemotherapy response using PET images and radiomics features," 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings, Sydney, Australia, 2018, pp. 1-3

2018

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.

[1] [2]

retrospective study on the health problems of falcons in Al Ain, United Arab Emirates

Al Hemeiri, M. A, Mohteshamuddin, K, de la Torre, A. A, Degafa, B. A and Ameni, G. A, “retrospective study on the health problems of falcons in Al Ain, United Arab Emirates”, Emirates Journal of Food and Agriculture, pp. 51-58, 2022

work page 2022

[2] [3]

Bird Disease Classification Using Pre - trained Convolutional Neural Networks

Yildiz, B and Aslan, M. M, “Bird Disease Classification Using Pre - trained Convolutional Neural Networks” in Applied Artificial Intelligence, pp. 1429-1448, 2022

work page 2022

[3] [4]

A Hybrid Deep Learning Approach for Automatic Bird Species Recogni tion from Images

Zhang Y, Liu S, Zhang W and Guo J, “A Hybrid Deep Learning Approach for Automatic Bird Species Recogni tion from Images” in Multimedia Tools and Applications, Journal, pp. 17083-17099

work page

[4] [5]

Transfer Learning for Bird Species Classification with Deep Convolutional Neural Networks

Van Beijl L and Moons T, “Transfer Learning for Bird Species Classification with Deep Convolutional Neural Networks” in Sensors, Journal, 21(13), 4405

work page

[5] [6]

Bird Disease Detection Using Deep Learning Techniques

A. Al-Hamad, A. A. Abouseif, A. A. Salem, and M. I. El-Hakeem, “Bird Disease Detection Using Deep Learning Techniques” in International Journal of Advanced Computer Science and Applications, 13(2), pp. 334- 343

work page

[6] [7]

M. S. Ali, M. S. Miah, J. Haque, M. M. Rahman, M. K. Islam, An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models, Machine Learning with Applications 5 (2021) 100036

work page 2021

[7] [8]

S. S. Chaturvedi, J. V. Tembhurne, T. Diwan, A multi -class skin cancer classification using deep convolutional neural networks, Multimedia Tools and Applications 79 (2020) pp. 28477–28498

work page 2020

[8] [9]

V. Balasubramaniam, Artificial intelligence algorithm with svm classification using dermascopic images for melanoma diagnosis, Journal of Artificial Intelligence and Capsule Networks 3 (2021) pp.34–42

work page 2021