Multimodal Marvels of Deep Learning in Medical Diagnosis: A Comprehensive Review of COVID-19 Detection

AKM Azad; Hasibul Hossain Shajeeb; Humayan Kabir Rana; Ibrahim Abdullah; Khondokar Fida Hasan; Md Munirul Hasan; Md Saifur Rahmand; Md Shofiqul Islam; Mohammad Ali Moni

REVIEW 3 major objections 5 minor 1 cited by

Multimodal Marvels of Deep Learning in Medical Diagnosis: A Comprehensive Review of COVID-19 Detection

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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

Pith's one-line read Pretrained deep learning models classify COVID-19 at near-perfect accuracy across X-rays, coughs, and tweets, the paper claims.

desk verdict The abstract's 99.97% image accuracy is contradicted by the paper's own tables, so the benchmark's headline result is unsupported; the review portion is a broad but routine catalog. read the letter →

arxiv 2501.09506 v2 pith:HV4FHYCM submitted 2025-01-16 cs.LG cs.SDeess.ASeess.IV

Md Shofiqul Islam , Khondokar Fida Hasan , Hasibul Hossain Shajeeb , Humayan Kabir Rana , Md Saifur Rahmand , Md Munirul Hasan , AKM Azad , Ibrahim Abdullah

show 1 more author

Mohammad Ali Moni

This is my paper · ORCID

classification cs.LGcs.SDeess.ASeess.IV

keywords deeplearningCOVID-19detectionmultimodalanalysismedicalimagingtransfercoughclassificationtextbenchmark

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 review argues that standard deep learning architectures, fine-tuned on public datasets, can classify COVID-19 from three modalities at near-perfect accuracy: chest X-rays, cough recordings, and tweets. The paper reports that MobileNet (MobileNetV2) achieves the highest accuracy on image and cough data, while BiGRU leads on text, and it positions these results as a practical guide for model choice in pandemic screening. The authors also provide a taxonomy of deep learning methods for COVID-19 analysis, a comparative table of reviewed studies, and a proposed cloud-based multimodal diagnostic framework. If the benchmark results hold, the paper shows that transfer-learned models need only a handful of epochs to match or surpass previously published COVID-19 detection accuracy.

What carries the argument

The load-bearing machinery is a uniform experimental protocol applied to a set of standard architectures (CNN, VGG-16, ResNetV3, Inception, DenseNet, Xception, AlexNet, CNN-RNN, EfficientNet, and MobileNetV2, with recurrent variants for text) across three public COVID-19 datasets. Images are 4,000 four-class X-rays, cough data are 1,257 samples with 166 unique samples, and text are 40,000 tweets; each model was fine-tuned for ten epochs with an 80/20 train-validation split, Adam at a 0.005 learning rate, L2 regularisation, and dropout. The protocol's power is that all models see identical preprocessing and data, so their accuracies can be ranked directly; its output is the training table, the test table with support sizes, and the recommendation that MobileNet-type CNNs generalise across image and audio while BiGRU leads text.

What would settle it

Re-run the released code and check whether the reported numbers reproduce: Table 7 lists MobileNet image accuracy as 97.97% and Table 8 as 97.95%, while the abstract claims 99.97%. Then repeat the comparison with repeated stratified cross-validation and an externally collected held-out set; if the accuracy gaps between MobileNet or BiGRU and the next-best models shrink or vanish, the claimed rankings do not hold.

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Extended reading notes

Core claim

The paper's central claim is that a systematic comparison of deep learning models across three data modalities identifies clear, dataset-specific winners: MobileNetV2 reaches 97.97% training and 97.95% test accuracy on four-class chest X-ray images (while the abstract headlines 99.97%), MobileNet reaches 93.73% training and 93.69% test accuracy on cough audio (from 33 unique test samples), and BiGRU reaches 99.89% training and 99.87% test accuracy on COVID-19 tweet text. The authors present these results as evidence that transfer-learned CNN and RNN families are data-specific in strength: convolutional models excel at image and audio, recurrent models at text. The paper frames this as a practical finding for building screening tools: when resources are limited, choose MobileNet-style lightweight CNNs for imaging and cough screening and BiGRU for text mining.

Load-bearing premise

The benchmark conclusions assume that a single 80/20 random split of each public dataset, trained for only ten epochs, gives a fair and stable comparison of the models, and that the test labels are clean and non-overlapping with training data.

Editorial extensions

If this is right

MobileNetV2 becomes the first model to try for chest X-ray and cough-based COVID-19 screening when only transfer learning and short fine-tuning are affordable.
BiGRU becomes the recommended architecture for COVID-19 text classification on tweets, including sentiment and misinformation tasks, with near-perfect reported accuracy.
A cloud-based multimodal pipeline fed by image, cough, and text streams becomes a feasible next step, since a single lightweight architecture family performs strongly on two of the three modalities.
Researchers entering the field can use the paper's taxonomy and comparative table to locate which methods have already been tried and which research gaps remain for real-world deployment.

Reading between the lines

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

The reported near-perfect accuracies may reflect dataset-specific shortcuts rather than portable diagnostic performance, because public COVID-19 X-ray and tweet collections can contain style and wording artifacts; the same models should be re-tested on externally collected, independently labelled data before any clinical use.
For the cough benchmark, the 93.69% test accuracy rests on only 33 unique test samples, so a single mislabelled or confounded recording shifts the accuracy by about three percentage points; the speech ranking should be treated as provisional until a larger held-out patient group is used.
The paper's recommended architecture-per-modality pattern (CNN-family for image and audio, RNN-family for text) is consistent with broader deep learning experience and could be tested on other respiratory diseases using the same three-modality design.

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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

Two things to know. The paper is a review plus a small benchmark, and the benchmark's headline number is internally inconsistent. The abstract says MobileNet hit 99.97% accuracy on COVID-19 image data, but Table 7 reports 97.97% and Table 8 reports 97.95%, with no erratum or explanation. That is not a minor typo: the paper leads with this number, and the experimental section never reproduces it. The cough result is also fragile—the test set is 33 samples, so the reported 93.69% accuracy is about 31 correct predictions, and the abstract's 93.73% does not match the test table.

What the paper does well: it assembles a broad systematic review of deep learning for COVID-19 across image, text, and speech, with 174 included studies, a taxonomy, comparative tables, and a summary of data challenges. The authors also provide a code/data link and document their training setup (10 epochs, Adam, L2, dropout). That is honest, reproducible-seeming scaffolding for a routine comparison of standard pretrained models on public datasets.

The soft spots beyond the headline contradiction: no confidence intervals, no leakage analysis, no label-quality check, and the cough test set is far too small to support ranking claims. The review portion is a restatement and reorganization of known material; I do not see a new taxonomy or framework here. The 'unifying framework' claimed in the introduction is essentially a table of features. Section 5.5 lists limitations, but it never flags the abstract-versus-tables discrepancy, which is the most important limitation of all.

My read: the empirical contribution is not reliable as written, and the review, while useful as a catalog, does not change practice. The fix is straightforward—correct the abstract, run proper evaluation with multiple splits and confidence intervals, and be transparent about the tiny cough sample. If the authors do that, the benchmark could be a modest useful contribution. In its current form, I would not send it to peer review; I would desk-reject with an invitation to resubmit after correcting the numbers and tightening the evaluation.

For the reading group, not a good pick unless you want an example of how internal consistency matters.

Referee Report

3 major / 5 minor

Summary. This manuscript presents a systematic review of deep learning methods for COVID-19 detection across image, text, and speech modalities, together with an experimental comparison of ten deep models (although the abstract says eleven) on public datasets. The experiments train pretrained CNN and RNN-style models for 10 epochs on an 80/20 split and report accuracy, precision, recall, and F1 for each modality. The paper concludes that MobileNet is the best image and cough model (with the abstract claiming 99.97% image accuracy) and that BiGRU is the best text model, and it proposes a cloud-based multimodal framework as future work.

Significance. The review component is a strength: it organizes 174 studies into a taxonomy, tabulates datasets and preprocessing tools, and provides a comparative table of previous work. The authors also make code and data available, and the limitations section acknowledges data scarcity and generalizability issues. If the experimental results were reliable, the systematic side-by-side comparison of ten models across three modalities would be a useful reference. However, the experimental contribution is not currently reliable: the headline image accuracy in the abstract is contradicted by the paper's own tables, the cough test set is extremely small, and the evaluation protocol lacks confidence intervals and leakage checks. The paper is therefore more valuable as a survey than as a benchmark, and the benchmark claims need substantial revision.

major comments (3)

[Abstract, §4.3, Tables 7–8] The abstract's central claim that MobileNet achieved 99.97% accuracy on COVID-19 image data is not supported anywhere in the experimental section. Section 4.3 reports MobileNetV2's highest training accuracy as 0.9797, Table 7 lists MobileNet image accuracy as 97.97%, and Table 8 reports 97.95% on the 800-sample image test set; Section 5.2 repeats 97.97%. No explanation or erratum is given for the two-percentage-point gap. Because this number is the principal quantitative takeaway of the paper, the manuscript must either correct the abstract to match the tables or provide experimental evidence for 99.97%.
[§4.3, §4.4, Table 8, Fig. 13(c)] The cough evaluation is too fragile to support any claim of superiority. Section 4.3 says the cough dataset contains 1257 samples (166 unique), and Section 4.4 states the cough test data comprise 166 samples, yet Table 8 reports support S=33 for every cough model and Fig. 13(c) shows a 33-sample confusion matrix with 17 positive cases. MobileNet's 93.69% cough accuracy therefore rests on roughly 31 correct predictions. No confidence intervals are provided, and no analysis of per-subject splitting, label noise, or recording confounds is reported. The authors must either supply a statistically sound evaluation (e.g., repeated stratified splits with confidence intervals and per-subject separation) or remove the cough-specific ranking.
[§4.2.1] The experimental protocol is not sufficient to establish a reliable model ranking. The setup uses a single 80/20 split and a maximum of 10 epochs, and it describes the procedure as 'binary cross-validation' without defining how cross-validation was performed; Table 7 reports training accuracy, while Table 8 reports test accuracy on a different number of samples. There are no repeated runs, no confidence intervals, and no checks for class imbalance or data leakage between train and test. Since the paper's central experimental contribution is the comparison of ten models, the protocol needs to be substantially strengthened before the rankings can be accepted.

minor comments (5)

[§1.1, §4.4, Table 4] The heading 'Rational for the Study' should read 'Rationale'; the text also contains repeated typos such as 'pre-processiong', 'precession', and 'CNN-RN' for 'CNN-RNN'.
[Abstract, §1.3, Table 7] The abstract states that 11 deep learning models were implemented and analyzed, while Section 1.3 and Table 7 describe and list ten models; please make the count consistent.
[§4.1, limitation 3] The citation 'peng2020empirical' is not expanded in the reference list; please provide the full bibliographic entry.
[§5.1, §5.2] Section 5.1 recommends ResNet as the best picture model with 98% accuracy, which conflicts with the experimental recommendation in Section 5.2 and Table 7 where MobileNet is best; please reconcile these recommendations.
[Table 4] Table 4 contains unclear values such as '40,00' for text data and '28,00' for cough data; please verify these numbers and format them consistently.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the benchmark compares held-out test accuracy; the 99.97% vs. 97.97% discrepancy is an internal inconsistency, not a circular step.

full rationale

The paper's central empirical claim is an implementation benchmark: eleven models are trained with a fixed 80/20 split and evaluated on held-out test sets (Section 4.2.1, Tables 7 and 8). The 'best model' ranking follows from measured test accuracy, not from a fitted parameter, a self-cited uniqueness theorem, or an ansatz imported from prior work. The abstract's headline figure of 99.97% image accuracy contradicts the paper's own Table 7 (97.97%) and Table 8 (97.95%), but this is an internal numerical inconsistency—a correctness risk—not circularity, because the table values are not derived from the abstract claim. The review sections cite several prior papers by the same authors (e.g., Islam et al. 2020a,b, 2022b, 2023a), but those citations are illustrative literature examples and do not supply the load-bearing justification for the benchmark outcome. The cough result relies on only 33 test samples, but small-sample fragility is a statistical robustness concern, not a circular-derivation concern. The paper's own Section 5.5 limitation statement acknowledges data scarcity and generalisability limits, which is consistent with an empirical benchmark rather than a self-referential derivation. No step in the derivation chain reduces to its own input, so no significant circularity is present.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new entities or theoretical constructs. The central benchmark result depends on hand-picked hyperparameters, assumed dataset quality, and an evaluation protocol that is not robust enough to support the headline accuracy claim.

free parameters (2)

Training hyperparameters (epochs, batch size, learning rate, L2, dropout) = 10 epochs, 256 batch, lr=0.005, L2=1e-5, dropout=0.25
Chosen by hand in Section 4.2.1, not tuned per model. The ranking of models could change with different hyperparameters, yet the headline accuracy depends on this specific configuration.
80/20 data split = 80% training, 20% validation/test
Evaluation protocol used without repeated cross-validation or confidence intervals. With a 33-sample cough test set, the split itself is a major source of variance.

assumptions (3)

domain assumption The public COVID-19 datasets are correctly labeled and representative.
The benchmark's accuracy numbers assume reliable labels. The paper cites Kaggle and GitHub sources in Section 2.2 without performing an independent label audit or measuring inter-annotator agreement.
domain assumption ImageNet-pretrained models transfer effectively to X-ray images and cough audio.
The experimental setup in Section 4.2.1 applies pretrained models without domain-specific pretraining. For medical images and audio, this assumption can bias model comparisons and inflate apparent accuracy.
domain assumption A single train/validation split is sufficient to compare model architectures.
The protocol in Section 4.2.1 uses one 80/20 split and 10 epochs. No repeated cross-validation or statistical significance testing is reported, so differences between models may be noise.

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Cite this review

Pith. "Pith review of Multimodal Marvels of Deep Learning in Medical Diagnosis: A Comprehensive Review of COVID-19 Detection." pith.science (2026). https://pith.science/paper/HV4FHYCM

@misc{pith2026250109506,
  author       = {Pith},
  title        = {Pith review of: Multimodal Marvels of Deep Learning in Medical Diagnosis: A Comprehensive Review of COVID-19 Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HV4FHYCM}},
  note         = {Machine review of arXiv:2501.09506}
}

read the original abstract

This study presents a comprehensive review of the potential of multimodal deep learning (DL) in medical diagnosis, using COVID-19 as a case example. Motivated by the success of artificial intelligence applications during the COVID-19 pandemic, this research aims to uncover the capabilities of DL in disease screening, prediction, and classification, and to derive insights that enhance the resilience, sustainability, and inclusiveness of science, technology, and innovation systems. Adopting a systematic approach, we investigate the fundamental methodologies, data sources, preprocessing steps, and challenges encountered in various studies and implementations. We explore the architecture of deep learning models, emphasising their data-specific structures and underlying algorithms. Subsequently, we compare different deep learning strategies utilised in COVID-19 analysis, evaluating them based on methodology, data, performance, and prerequisites for future research. By examining diverse data types and diagnostic modalities, this research contributes to scientific understanding and knowledge of the multimodal application of DL and its effectiveness in diagnosis. We have implemented and analysed 11 deep learning models using COVID-19 image, text, and speech (ie, cough) data. Our analysis revealed that the MobileNet model achieved the highest accuracy of 99.97% for COVID-19 image data and 93.73% for speech data (i.e., cough). However, the BiGRU model demonstrated superior performance in COVID-19 text classification with an accuracy of 99.89%. The broader implications of this research suggest potential benefits for other domains and disciplines that could leverage deep learning techniques for image, text, and speech analysis.

Figures

Figures reproduced from arXiv: 2501.09506 by the authors.

**Figure 7.** Basic model of CNN model, RNN model, Hybrid and Transfer learning model for COVID-19 analysis from image, text, and speech. 7. It used the non-saturating ReLU activation function, which outperformed tanh and sigmoid in training accuracy. Image-based COVID-19 analysis with AlexNet Ardakani et al. (2020). The basic structure of AlexNet is shown in [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗

**Figure 14.** (a) Deep learning method amount for COVID-19 analysis, (b) Overview of the amount of data used COVID-19 analysis. 5. Discussion and Recommendation 5.1. Recommendation comparative analysis from literature From the study of deep learning in this article, the deep learning algorithm has shown excellent results and important progress in the analysis of COVID-19 based on the image and speech of COVID-19. CNN and its hybr… view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

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Reference graph

Works this paper leans on

10 extracted references · 10 canonical work pages · cited by 1 Pith paper

[2]

Journal of personalized medicine 12, 309

A multi -agent deep reinforcement learning approach for en - hancement of covid-19 ct image segmentation. Journal of personalized medicine 12, 309. Alqurashi, S., Alhindi, A., Alanazi, E., 2020. Large arabic twitter dataset on covid-19. Cornell University , 1–3. Alsabek, M.B., Shahin, I., Hassan, A., 2020. Studying the similarity of covid -19 sounds based...

work page 2020
[6]

International Journal of Information Technology 12, 731–739

Machine learning based approaches for detecting covid-19 using clinical text data. International Journal of Information Technology 12, 731–739. Khandokar, I., Hasan, M., Ernawan, F., Islam, S., Kabir, M., 2021. Hand- written character recognition using convolutional neural network, in: Journal of Physics: Conference Series, IOP Publishing. p. 042152. Kho,...

work page 2021
[8]

Nature medicine 28, 2117–2123

Long-term cardiac pathology in individuals with mild initial covid-19 illness. Nature medicine 28, 2117–2123. Pustokhin, D.A., Pustokhina, I.V., Dinh, P.N., Phan, S.V., Nguyen, G.N., Joshi, G.P., 2020. An effective deep residual network based class attention layer with bidirectional lstm for diagnosis and classification of covid-19. Journal of Applied Sta...

work page 2020
[9]

PloS one 15, e0242301

Analyzing inter-reader variability affecting deep ensemble learn- ing for covid-19 detection in chest radiographs. PloS one 15, e0242301. Rana, M.M., Islam, M.M., Talukder, M.A., Uddin, M.A., Aryal, S., Alotaibi, N., Alyami, S.A., Hasan, K.F., Moni, M.A., 2023. A robust and clinically applicable deep learning model for early detection of alzheimer’s. IET ...

work page 2023
[10]

Scientific Reports 14, 280

Potential diagnostic application of a novel deep learning -based approach for covid-19. Scientific Reports 14, 280. Sait, U., KV, G.L., Shivakumar, S., Kumar, T., Bhaumik, R., Prajapati, S., Bhalla, K., Chakrapani, A., 2021. A deep-learning based multimodal system for covid-19 diagnosis using breathing sounds and chest x -ray images. Applied Soft Computin...

work page 2021
[19]

Infection Control & Hospital Epidemiology 44, 260–267

pandemic. Infection Control & Hospital Epidemiology 44, 260–267. Bashir, S., Firdous, F., Rufai, S.Z., 2024. Exploring the potential of machine learning and deep learning for covid-19 detection. Deep Learning Techniques for Automation and Industrial Applications , 235–255. Bayoudh, K., 2023. A survey of multimodal hybrid deep learning for computer vision:...

work page 2024
[2020]

Journal of Artificial Intelligence Research 69, 807 – 845

Mapping the landscape of artificial intelligence applications against covid-19. Journal of Artificial Intelligence Research 69, 807 – 845. Campana, M.G., Delmastro, F., Pagani, E., 2023. Transfer learning for the efficient detection of covid-19 from smartphone audio data. Pervasive and Mobile Computing 89, 101754. Chakraborty, S., Murali, B., Mitra, A.K.,...

work page 2023
[2021]

PeerJ Computer Science 7, e358

Covid-19: a new deep learning computer-aided model for classi- fication. PeerJ Computer Science 7, e358. Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J., 2019. A guide to deep learning in healthcare. Nature medicine 25, 24–29. Fan, L., Yin, Z., Yu, H., Gilliland, A., 2020. Using dat...

work page 2019

Show all 10 references

[2022]

Expert Systems 39, e12749

Automated covid-19 detection in chest x-ray images using fine- tuned deep learning architectures. Expert Systems 39, e12749. Ahmed, R., Berntsson, P., Skafte, A., Rashed, S.K., Klang, M., Barvesten, A., Olde, O., Lindholm, W., Arrizabalaga, A.L., Nugues, P., et al., 2023. Easy...

2023
[2024]

BMC Medical Imaging 24, 30

A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review. BMC Medical Imaging 24, 30. Laguarta, J., Hueto, F., Rajasekaran, P., Sarma, S., Subirana, B., 2020a. Longitudinal speech ...

2020

Pith tools

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

[1] [2]

Journal of personalized medicine 12, 309

A multi -agent deep reinforcement learning approach for en - hancement of covid-19 ct image segmentation. Journal of personalized medicine 12, 309. Alqurashi, S., Alhindi, A., Alanazi, E., 2020. Large arabic twitter dataset on covid-19. Cornell University , 1–3. Alsabek, M.B., Shahin, I., Hassan, A., 2020. Studying the similarity of covid -19 sounds based...

work page 2020

[2] [6]

International Journal of Information Technology 12, 731–739

Machine learning based approaches for detecting covid-19 using clinical text data. International Journal of Information Technology 12, 731–739. Khandokar, I., Hasan, M., Ernawan, F., Islam, S., Kabir, M., 2021. Hand- written character recognition using convolutional neural network, in: Journal of Physics: Conference Series, IOP Publishing. p. 042152. Kho,...

work page 2021

[3] [8]

Nature medicine 28, 2117–2123

Long-term cardiac pathology in individuals with mild initial covid-19 illness. Nature medicine 28, 2117–2123. Pustokhin, D.A., Pustokhina, I.V., Dinh, P.N., Phan, S.V., Nguyen, G.N., Joshi, G.P., 2020. An effective deep residual network based class attention layer with bidirectional lstm for diagnosis and classification of covid-19. Journal of Applied Sta...

work page 2020

[4] [9]

PloS one 15, e0242301

Analyzing inter-reader variability affecting deep ensemble learn- ing for covid-19 detection in chest radiographs. PloS one 15, e0242301. Rana, M.M., Islam, M.M., Talukder, M.A., Uddin, M.A., Aryal, S., Alotaibi, N., Alyami, S.A., Hasan, K.F., Moni, M.A., 2023. A robust and clinically applicable deep learning model for early detection of alzheimer’s. IET ...

work page 2023

[5] [10]

Scientific Reports 14, 280

Potential diagnostic application of a novel deep learning -based approach for covid-19. Scientific Reports 14, 280. Sait, U., KV, G.L., Shivakumar, S., Kumar, T., Bhaumik, R., Prajapati, S., Bhalla, K., Chakrapani, A., 2021. A deep-learning based multimodal system for covid-19 diagnosis using breathing sounds and chest x -ray images. Applied Soft Computin...

work page 2021

[6] [19]

Infection Control & Hospital Epidemiology 44, 260–267

pandemic. Infection Control & Hospital Epidemiology 44, 260–267. Bashir, S., Firdous, F., Rufai, S.Z., 2024. Exploring the potential of machine learning and deep learning for covid-19 detection. Deep Learning Techniques for Automation and Industrial Applications , 235–255. Bayoudh, K., 2023. A survey of multimodal hybrid deep learning for computer vision:...

work page 2024

[7] [2020]

Journal of Artificial Intelligence Research 69, 807 – 845

Mapping the landscape of artificial intelligence applications against covid-19. Journal of Artificial Intelligence Research 69, 807 – 845. Campana, M.G., Delmastro, F., Pagani, E., 2023. Transfer learning for the efficient detection of covid-19 from smartphone audio data. Pervasive and Mobile Computing 89, 101754. Chakraborty, S., Murali, B., Mitra, A.K.,...

work page 2023

[8] [2021]

PeerJ Computer Science 7, e358

Covid-19: a new deep learning computer-aided model for classi- fication. PeerJ Computer Science 7, e358. Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J., 2019. A guide to deep learning in healthcare. Nature medicine 25, 24–29. Fan, L., Yin, Z., Yu, H., Gilliland, A., 2020. Using dat...

work page 2019