Robust COVID-19 Detection from Cough Sounds using Deep Neural Decision Tree and Forest: A Comprehensive Cross-Datasets Evaluation

Muhammad Ashad Kabir; Nihad Karim Chowdhury; Rofiqul Islam

REVIEW 4 major objections 5 minor 125 references

Robust COVID-19 Detection from Cough Sounds using Deep Neural Decision Tree and Forest: A Comprehensive Cross-Datasets Evaluation

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper claims a deep neural decision forest with selected spectral features and tuned thresholds separates COVID-19 coughs at AUC 0.97-0.99 per dataset and 0.97 pooled, while cross-dataset transfer drops sharply.

desk verdict The cross-dataset matrix is a useful negative result, but the headline AUCs are inflated by fitting features, hyperparameters, and thresholds on the full data before CV. read the letter →

arxiv 2501.01117 v1 pith:XNXHDHT7 submitted 2025-01-02 cs.SD cs.AIcs.LGeess.AS

Rofiqul Islam , Nihad Karim Chowdhury , Muhammad Ashad Kabir This is my paper · ORCID

classification cs.SDcs.AIcs.LGeess.AS

keywords COVID-19detectioncoughsoundanalysisdeepneuraldecisionforesttreecross-datasetsevaluationRFECVfeatureselectionBayesianoptimizationaudioclassification

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

The paper tries to establish that a cough recording alone can flag COVID-19 with high accuracy when classified by a differentiable decision forest, provided the audio is summarized by five spectral feature families and the model is tuned with the right feature subset, hyperparameters, class rebalancing, and decision threshold. It reports AUCs of 0.97, 0.98, 0.92, 0.93, 0.99, and 0.99 on Cambridge asymptomatic, Cambridge symptomatic, Coswara, COUGHVID, Virufy, and Virufy combined with NoCoCoDa, and 0.97 when all datasets are pooled. It also shows that models trained on one dataset and tested on another perform much worse, which it reads as demographic and geographic variation in cough acoustics and as evidence that pooled training helps generalization. If true, the work supports low-cost, app-based cough screening for COVID-19, not a standalone diagnosis.

What carries the argument

The central object is the deep neural decision forest (DNDF), an ensemble of differentiable decision trees whose split decisions are sigmoid functions of learned features; the paper averages each tree's class distribution for the final prediction. Around this sits a five-stage pipeline: extraction of 193 spectral features (40 MFCCs, 128 mel-scaled spectrogram values, 6 tonal centroid values, 12 chromagram values, and 7 spectral contrast values), RFECV with an Extra-Trees estimator to select a dataset-specific feature subset, Bayesian optimization for hyperparameters, SMOTE to rebalance positive and negative coughs, and threshold moving over 0.1 to 1.0 to maximize ROC-AUC. The paper's evidence that this machinery matters is its strategy ladder, where the same classifiers climb from near-chance AUC with raw defaults to the reported highs as each component is added.

What would settle it

Run a patient-level grouped cross-validation on the Virufy dataset, holding out entire patients rather than individual cough samples, and move feature selection and threshold selection inside the training folds; if the reported 0.99 AUC drops substantially, the claimed generalization depends on sample-level leakage.

Watch

Extended reading notes

Core claim

The paper's central claim is that a specific training recipe, DNDF with RFECV feature selection, Bayesian optimization, SMOTE, and threshold moving, beats published cough-based COVID-19 classifiers on each of six dataset splits. The headline numbers are AUCs of 0.97 on Cambridge asymptomatic, 0.98 on Cambridge symptomatic, 0.92 on Coswara, 0.93 on COUGHVID, 0.99 on Virufy, and 0.99 on Virufy merged with NoCoCoDa, with precision scores of 1, 1, 0.72, 0.93, 1, and 1, respectively. On the pooled dataset of 3,398 coughs, the forest variant reaches accuracy 0.97, AUC 0.97, precision 0.95, recall 0.96, F1-score 0.96, and specificity 0.97. The paper also reports a cross-dataset study in which the same method is trained on one dataset and tested on the others; those numbers are mostly far lower, which the authors interpret as demographic and geographic variation in cough acoustics and as evidence that dataset integration improves generalizability. The claim is about the achieved metrics under the stated protocol, not about deployment in a clinic.

Load-bearing premise

The evaluation assumes that 10-fold stratified cross-validation, with feature selection, hyperparameter tuning, and threshold selection performed on the same folds and with cough samples from the same patient allowed in different folds, gives an unbiased estimate of how the model would perform on new people.

Editorial extensions

If this is right

Each element of strategy 5 contributes: threshold moving alone lifts AUC above the raw classifier, and adding RFECV, Bayesian optimization, and SMOTE improves it further on nearly every dataset.
The forest version beats the single tree on every dataset, which the authors attribute to the ensemble reducing prediction variance.
Pooling all five datasets into one training set yields the best overall balance (accuracy, AUC, precision, recall, F1, and specificity all around 0.95-0.97), supporting dataset integration.
The cross-dataset results, with several AUCs near 0.5, indicate that a model trained on one cohort cannot be assumed to transfer to another cohort; matched demographics or recording conditions matter.
Taken at face value, the results support cough-based triage or screening in settings where the test population resembles the training population, not a standalone diagnostic.

Reading between the lines

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

Beyond the paper: the near-perfect scores on Virufy and Virufy plus NoCoCoDa may be inflated by patient-level leakage, since Virufy holds 121 samples from only 16 patients and the same Virufy recordings also appear in the merged set.
Beyond the paper: the paper's own cross-dataset table is evidence that much of what the model learns is cohort-specific, so a deployment study should measure calibration on the target population before trusting the reported AUC.
Beyond the paper: a perturbation test, such as resampling, adding noise, or changing microphone type, would show whether a combined AUC of 0.97 reflects an acoustic disease signature or a dataset fingerprint.

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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

The genuinely useful thing here is Table 9: a systematic cross-dataset evaluation across five cough datasets showing that non-overlapping training and testing sets give near-chance AUCs (0.51–0.73). That negative result aligns with the known critiques of COVID-19 audio classifiers (Coppock et al., Akman et al.) and is a real empirical contribution. The paper also deserves credit for stating that poor transfer is due to recording conditions and demographic differences, and for citing the bias literature rather than ignoring it.

The problem is the intradataset evaluation. RFECV feature selection, Bayesian hyperparameter optimization, and ROC-AUC threshold moving are all applied once to the entire dataset before forming the 10 folds. That means the test fold's labels influence which features, hyperparameters, and thresholds are chosen, so the reported AUCs of 0.92–0.99 are not unbiased estimates of generalization. No nested CV, no held-out set, no error bars. The paper's own cross-dataset results confirm the risk: every non-overlapping pair is near chance, and the only high transfer numbers (0.85, 0.87) come when Virufy samples appear in both training and test sets. The text explicitly acknowledges the Virufy overlap, so this is leakage, not evidence of transfer.

A few smaller issues: the comparison with prior work is limited to three studies, which narrows the claimed 'consistent outperformance'; the abstract and Table 10 disagree slightly on the combined-dataset specificity (not stated in abstract, but the AUC matches); and the precision values in the abstract (1, 1, 0.72, 0.93, 1, 1) are perfectly explained by the confusion matrices but would still benefit from confidence intervals, especially on the small Virufy dataset (121 samples from 16 patients).

In short: the negative cross-dataset finding is worth publishing, but the headline claim of robust, consistent superiority is not supported by the current protocol. The authors would need to redo the evaluation with nested CV, participant-level splits, and no overlapping subsets before those AUCs can be taken seriously. That is a heavy revision, but the negative result itself is a useful caution for the field.

I would send this to peer review: a good referee can separate the flawed intradataset protocol from the valid cross-dataset message. I would not desk-reject it, but I would insist on the redesign. The paper is clearly written and the authors are honest about the transfer failures, so it is a serious piece of work despite the methodological flaw.

Referee Report

4 major / 5 minor

Summary. The paper proposes a cough-sound-based COVID-19 detection pipeline built on deep neural decision trees (DNDT) and deep neural decision forests (DNDF), combined with RFECV feature selection, Bayesian hyperparameter optimization, SMOTE, and ROC-AUC-based threshold moving. The authors evaluate the method on Cambridge asymptomatic/symptomatic, Coswara, COUGHVID, Virufy, and Virufy merged with NoCoCoDa, report intradataset AUCs of 0.92–0.99, perform a cross-dataset transfer study, and report an AUC of 0.97 on a combined dataset. The central claim is that the proposed pipeline consistently outperforms state-of-the-art methods and is robust across diverse cough datasets.

Significance. If the reported results were valid, the paper would provide a useful datapoint for cough-based COVID-19 screening and for comparing tree-based learned decision models with deep classifiers on audio features. The manuscript has several commendable features: it covers five widely used cough datasets, reports a full cross-dataset matrix, evaluates five incremental training strategies, and includes a broad comparative table of prior work. However, the empirical contribution is not machine-checkable (no code or data release is described), and the central evaluation protocol is invalid as reported. The claimed consistent superiority over prior work and the claimed robustness are not supported by the evidence actually presented in Tables 8 and 9. The cross-dataset results, which the paper itself acknowledges to be poor, are more consistent with dataset-specific shortcuts than with a reusable cough-COVID signature.

major comments (4)

[§4.3–§4.7, Tables 6–7] The evaluation protocol does not support the reported generalization performance. Section 4.3 applies RFECV feature selection to the full dataset to select feature subsets (71, 182, 33, 172, 46, and 188 features for the six datasets), Section 4.4 uses Bayesian optimization on the full extracted features and labels, and Section 4.6 selects the decision threshold by maximizing ROC-AUC over the full dataset. All of these steps are performed before the 10-fold stratified cross-validation described in Section 4.7. Consequently, the test folds contribute to feature choice, hyperparameter choice, and threshold choice, so the resulting AUCs are optimistically biased and are not unbiased estimates of performance on new data. The paper needs either a nested cross-validation scheme in which all model selection is performed inside each training fold, or a single independent test set withheld before any selection or tuning. The absence of any error bars or variance estimates further weakens the headline claims in Tables 6 and 7.
[§5.4, Table 9, Table 2] The cross-dataset results contradict the paper's claim of robustness. Most non-overlapping training/testing pairs in Table 9 yield AUCs between 0.51 and 0.73; for example, Cambridge (Asymptomatic) trained on COUGHVID gives 0.51, Coswara trained on Cambridge (Symptomatic) gives 0.52, and COUGHVID trained on Cambridge (Asymptomatic) gives 0.54. The only high cross-dataset values are Virufy → Virufy+NoCoCoDa (0.85) and Virufy+NoCoCoDa → Virufy (0.87), but Table 2 shows that Virufy+NoCoCoDa is constructed by adding NoCoCoDa samples to the Virufy samples, so these two datasets share their Virufy instances and the high values reflect sample overlap rather than transfer. The paper's own text in Section 5.4 acknowledges the poor cross-dataset performance, and the combined-dataset result in Section 5.5 does not remedy this because it is evaluated with the same flawed protocol and mixes all datasets into one training pool.
[§5.3, Table 8] The claim that the proposed method is 'consistently outperforming state-of-the-art methods' is not supported by Table 8. On Coswara, the proposed DNDT achieves AUC 0.84, below Zhang et al.'s 0.86. On COUGHVID, the proposed DNDT achieves 0.81, below Hamdi et al.'s 0.91 and Skander et al.'s 0.91; the proposed DNDF's 0.93 does not exceed those values. On Cambridge Asymptomatic, DNDF's 0.97 ties Aytekin et al., and DNDT's 0.95 is lower; on Cambridge Symptomatic, DNDF's 0.98 ties Aytekin et al.; on Virufy+NoCoCoDa, both proposed methods tie Melek's AUC of 0.99. Thus the comparison table shows at best parity on several datasets and outright inferiority on others, so the abstract's and conclusion's claims of consistent superiority are overstated.
[§4.1.4–§4.1.5, §5.2] The use of sample-level 10-fold stratified cross-validation is not justified for datasets with multiple recordings from the same subject. Section 4.1.4 states that Virufy contains 121 cough samples from 16 patients, and Section 4.1.5 states that NoCoCoDa contains 73 cough sounds from 10 participants. When recordings from the same patient can appear in both training and test folds, the model can exploit patient-specific recording characteristics rather than a general COVID-19 cough signature. This is especially relevant for the near-perfect AUCs of 0.99 reported for Virufy and Virufy+NoCoCoDa in Table 6. The authors should either perform subject-level splitting, where all samples from one participant are kept in the same fold, or explicitly justify why sample-level independence is appropriate for these data.

minor comments (5)

[Abstract and body text] There are repeated typographical errors, including 'di fferences' in the abstract and 'V olume' in the references, which should be corrected.
[§4.1 and §4.2] The stated sampling rate is inconsistent: Section 4.1 says resampling at 22.5 kHz, while Section 4.2 says the acoustic signal is captured at 22 kHz. The authors should state which value was actually used.
[Figure 5] The caption and text for Figure 5(d) describe a percentage as '92.65' without the percent sign; this should be fixed.
[Table 2] The Virufy+NoCoCoDa row counts (121 COVID-19, 73 non-COVID-19) make the overlap with Virufy explicit, but the table would be clearer if it noted that the 121 COVID-19 samples are exactly the union of Virufy's 48 and NoCoCoDa's 73 positives.
[§5.4] The sentence 'We examine the DNDF classifier in this context' is vague; the paper should state explicitly why only DNDF, and not DNDT, is used for the cross-dataset study.

Circularity Check

3 steps flagged · score 6.0 of 10

Intradataset AUCs are selection-biased because RFECV feature selection, Bayesian hyperparameter optimization, and threshold choice are applied to the full dataset before the 10-fold CV that is reported as generalization.

fitted input called prediction [Section 4.3 and Section 4.7 (Tables 6-7)]
"Following the use of the RFECV technique and Extra-Trees Classifier, we obtain optimal features of 71, 182, 33, 172, 46, and 188 for the Cambridge asymptomatic, Cambridge symptomatic, Coswara, COUGHVID, Virufy, and Virufy merged with NoCoCoDa datasets, respectively. ... We also use 10-fold stratified cross-validation to assess the performance of our trained classifiers in all strategies."

The optimal feature subset is selected once per dataset using all samples and labels, before the 10-fold split. The folds used to compute the reported AUC therefore contribute their labels to the feature-selection input. The high intradataset AUCs in Tables 6-7 are not unbiased estimates of generalization; they are partly constructed from the evaluation data. No nested cross-validation or independent holdout is described.
fitted input called prediction [Section 4.4, Table 4, and Section 5.1]
"Extracted input features and their labels are given to the Bayesian Optimization function to obtain the most effective hyper-parameter values. ... In our experimental evaluation, we use 10-fold stratified cross-validation to evaluate the performance using six standard evaluation metrics: ROC-AUC, Accuracy, Precision, Recall /Sensitivity, Specificity, and F1 score."

The hyperparameters reported in Table 4 are optimized per dataset on the full labeled dataset, and the same dataset's 10-fold CV is then presented as performance. Because the test fold's labels participate in choosing depth, number of trees, learning rate, batch size, and epochs, the reported metrics are fit statistics rather than out-of-sample predictions. This directly inflates the headline AUC values.

1 more flagged steps

fitted input called prediction [Section 4.6 and Section 4.7]
"ROC-AUC score-based threshold optimization is achieved by cross-validation tests. We calculate ROC-AUC scores over a threshold value range of 0.1 to 1, using 0.001 increments. The best threshold is then determined by selecting the one that produced the highest ROC-AUC score. ... The optimal threshold, hyper-parameters, and selected features are subsequently input into classifiers (DNDF and DNDT) for COVID-19 detection and to assess the proposed method."

The threshold is selected using the same labeled data that later generates the reported confusion matrices and precision, recall, and accuracy values. Those operating-point metrics are therefore not independent predictions but reflect a threshold fitted to the evaluation data. Since ROC-AUC itself is threshold-independent, this step mainly biases the confusion-matrix metrics, while the RFECV and BO steps bias the AUC directly.

full rationale

The derivation chain is not self-citational in a load-bearing way: references [58] and [109] involve the present authors, but they are used as comparison baselines rather than as premises, so they do not create circularity. The central circularity is statistical. Sections 4.3-4.7 describe feature selection (RFECV), hyperparameter tuning (Bayesian optimization), and threshold selection (ROC-AUC-based threshold moving) applied to the full dataset before the 10-fold stratified CV is used to report performance. The same data then appears in the reported confusion matrices and AUCs, so the headline values 0.97, 0.98, 0.92, 0.93, 0.99, and 0.99 partly encode optimization on the evaluation distribution. The paper's own cross-dataset results in Table 9 corroborate this reading: transfer AUCs are near chance (0.51-0.73) except when Virufy samples are shared between training and test sets (Virufy vs. Virufy+NoCoCoDa). That pattern is consistent with the high intradataset values reflecting dataset-specific fitted structure rather than a reusable cough-COVID signature. Because the final classifier still trains on only 90% of the data in each fold, the circularity is partial rather than complete, supporting a score of 6 rather than 8 or 10.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The reported success depends on several unvalidated assumptions about label quality, evaluation protocol, and dataset composition. The free parameters are hyperparameters, thresholds, and feature subsets fit to the same data used for evaluation.

free parameters (4)

Per-dataset DNDT/DNDF hyperparameters = num_trees 16-42, depth 5-16, feature_rate 0.6-1.0, batch 8-256, epochs 13-40 (Table 4)
Selected by Bayesian optimization on each dataset; evaluation then reports CV AUC using these same datasets, so the optimization is not held out.
Decision threshold = Not reported; scanned 0.1 to 1.0 in 0.001 steps
Chosen as the value maximizing ROC-AUC per Section 4.6; this is a free parameter fit to the evaluation data.
RFECV-selected feature subset size = 71, 182, 33, 172, 46, 188 features for the six dataset variants
Feature subsets are chosen on the full data before CV, creating a selection bias that affects the reported performance.
SMOTE oversampling ratio = Not reported
SMOTE is applied during training to balance classes, but the sampling ratio is not specified; this unstated choice affects results.

assumptions (4)

domain assumption Crowdsourced self-reported COVID-19 labels are reliable enough to serve as ground truth
Used for Cambridge, Coswara, COUGHVID, and Virufy labels in Section 4.1; COUGHVID and Coswara rely on self-report.
ad hoc to paper Sample-level 10-fold stratified CV on the same datasets used for feature, hyperparameter, and threshold selection yields unbiased generalization estimates
Sections 4.7 and 5.2 describe the CV protocol, but no nested CV or held-out split is described.
ad hoc to paper Combining five datasets into one pool is valid despite different recording devices, languages, and possible participant overlap
Section 5.5 uses the combined dataset; Virufy samples appear in both Virufy and Virufy plus NoCoCoDa rows of Table 9.
domain assumption There exists a stable acoustic COVID-19 signature in coughs beyond dataset-specific correlations
Central premise of the paper; cross-dataset Table 9 shows AUC near chance for most transfers, so this axiom is weakly supported.

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Cite this review

Pith. "Pith review of Robust COVID-19 Detection from Cough Sounds using Deep Neural Decision Tree and Forest: A Comprehensive Cross-Datasets Evaluation." pith.science (2026). https://pith.science/paper/XNXHDHT7

@misc{pith2026250101117,
  author       = {Pith},
  title        = {Pith review of: Robust COVID-19 Detection from Cough Sounds using Deep Neural Decision Tree and Forest: A Comprehensive Cross-Datasets Evaluation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XNXHDHT7}},
  note         = {Machine review of arXiv:2501.01117}
}

read the original abstract

This research presents a robust approach to classifying COVID-19 cough sounds using cutting-edge machine-learning techniques. Leveraging deep neural decision trees and deep neural decision forests, our methodology demonstrates consistent performance across diverse cough sound datasets. We begin with a comprehensive extraction of features to capture a wide range of audio features from individuals, whether COVID-19 positive or negative. To determine the most important features, we use recursive feature elimination along with cross-validation. Bayesian optimization fine-tunes hyper-parameters of deep neural decision tree and deep neural decision forest models. Additionally, we integrate the SMOTE during training to ensure a balanced representation of positive and negative data. Model performance refinement is achieved through threshold optimization, maximizing the ROC-AUC score. Our approach undergoes a comprehensive evaluation in five datasets: Cambridge, Coswara, COUGHVID, Virufy, and the combined Virufy with the NoCoCoDa dataset. Consistently outperforming state-of-the-art methods, our proposed approach yields notable AUC scores of 0.97, 0.98, 0.92, 0.93, 0.99, and 0.99 across the respective datasets. Merging all datasets into a combined dataset, our method, using a deep neural decision forest classifier, achieves an AUC of 0.97. Also, our study includes a comprehensive cross-datasets analysis, revealing demographic and geographic differences in the cough sounds associated with COVID-19. These differences highlight the challenges in transferring learned features across diverse datasets and underscore the potential benefits of dataset integration, improving generalizability and enhancing COVID-19 detection from audio signals.

Figures

Figures reproduced from arXiv: 2501.01117 by the authors.

**Figure 2.** Overview of the extraction process for 193 features across five types: MFCCs, Mel-Scaled Spectrogram, Tonal Centroid, Chromagram, [PITH_FULL_IMAGE:figures/full_fig_p014_2.png] view at source ↗

**Figure 3.** Feature selection with the recursive feature elimination with cross-validation (RFECV) and Extra-Trees classifier. [PITH_FULL_IMAGE:figures/full_fig_p016_3.png] view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: The confusion matrices for strategy 5, using the Deep Neural Decision Tree (DNDT) classifier with 10-fold cross-validation: (a) Cam [PITH_FULL_IMAGE:figures/full_fig_p023_4.png]

**Figure 5.** Figure 5: The confusion matrices for strategy 5, using the Deep Neural Decision Forest (DNDF) classifier with 10-fold cross-validation: (a) [PITH_FULL_IMAGE:figures/full_fig_p024_5.png]

**Figure 6.** Figure 6: The distribution of MFCC features across COVID-19 positive and negative samples across several datasets. [PITH_FULL_IMAGE:figures/full_fig_p030_6.png]

**Figure 7.** Figure 7: The confusion matrices for (a) the DNDT classifier and (b) the DNDF classifier, using the combined dataset, are generated through [PITH_FULL_IMAGE:figures/full_fig_p032_7.png]

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

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