# Chronic Classification (3 classes) on ICBHI challenge dataset 2017 (test)

Source: [Chronic Classification (3 classes) on ICBHI challenge dataset 2017 (test)](<https://www.sota2.com/research/sota/chronic-classification-3-classes-on-icbhi-challenge-dataset-2017-test>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-02-26T05:27:06.689Z

Evaluation of 3-class chronic disease classification performance on the test split of the ICBHI challenge dataset 2017.

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

| Scope | Recorded value |
| --- | --- |
| Task | Chronic classification (3 classes) |
| Dataset (including any stated split) | ICBHI challenge dataset 2017 (test) |
| Metric | Accuracy |
| Unit | PERCENT |
| Direction | Higher is better |
| Results loaded | 6 of 6 |
| Coverage | All results currently returned for this benchmark |

Evaluation settings may differ between results. The original papers and recorded configurations are needed to establish comparability. A shared metric name alone does not establish an identical protocol.

## Metrics

- [Accuracy](<https://www.sota2.com/research/sota/chronic-classification-3-classes-on-icbhi-challenge-dataset-2017-test.md?metric=Accuracy>): Higher is better; unit: PERCENT.

## Recorded results

| Method | Accuracy | Result date | Report paper |
| --- | --- | --- | --- |
| AlexNet | 99.519 | Not recorded | 68fa7f781e209131cea9a3f4 |
| Proposed model | 99.21 | Not recorded | 68fa7f781e209131cea9a3f4 |
| ShuffleNet (v2) | 99.06 | Not recorded | 68fa7f781e209131cea9a3f4 |
| MobileNet (v2) | 98.72 | Not recorded | 68fa7f781e209131cea9a3f4 |
| NASNet | 98.42 | Not recorded | 68fa7f781e209131cea9a3f4 |
| VGG16 | 97.6 | Not recorded | 68fa7f781e209131cea9a3f4 |

### AlexNet

Accuracy: 99.519. Result ID: 698b93407a967eddb76fb746.

[Reporting paper](<https://www.sota2.com/research/paper/68fa7f781e209131cea9a3f4.md>)

Recorded evaluation configuration: {"Size (after training)":"294MB","Trainable parameters":"25.704M","MAdd":"725M"}. This record does not assert independent reproduction.

### Proposed model

Accuracy: 99.21. Result ID: 698b93407a967eddb76fb747.

[Reporting paper](<https://www.sota2.com/research/paper/68fa7f781e209131cea9a3f4.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa7f781e209131cea9a3f4.md>)

Recorded evaluation configuration: {"Size (after training)":"44.85MB","Trainable parameters":"3.7674M","MAdd":"371.93M"}. This record does not assert independent reproduction.

### ShuffleNet (v2)

Accuracy: 99.06. Result ID: 698b93407a967eddb76fb749.

[Reporting paper](<https://www.sota2.com/research/paper/68fa7f781e209131cea9a3f4.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa64a61e209131cea05ddb.md>)

Recorded evaluation configuration: {"Size (after training)":"46.9MB","Trainable parameters":"5.4M","MAdd":"564M"}. This record does not assert independent reproduction.

### MobileNet (v2)

Accuracy: 98.72. Result ID: 698b93407a967eddb76fb748.

[Reporting paper](<https://www.sota2.com/research/paper/68fa7f781e209131cea9a3f4.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa65811e209131cea0a9a1.md>)

[Implementation](<https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet>)

Recorded evaluation configuration: {"Size (after training)":"49MB","Trainable parameters":"4.2M","MAdd":"575M"}. This record does not assert independent reproduction.

### NASNet

Accuracy: 98.42. Result ID: 698b93407a967eddb76fb74a.

[Reporting paper](<https://www.sota2.com/research/paper/68fa7f781e209131cea9a3f4.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa67951e209131cea172ff.md>)

Recorded evaluation configuration: {"Size (after training)":"64MB","Trainable parameters":"4.2M","MAdd":"567M"}. This record does not assert independent reproduction.

### VGG16

Accuracy: 97.6. Result ID: 698b93407a967eddb76fb745.

[Reporting paper](<https://www.sota2.com/research/paper/68fa7f781e209131cea9a3f4.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa61d01e209131ce9f62f5.md>)

[Implementation](<http://www.robots.ox.ac.uk/~vgg/research/very_deep/>)

Recorded evaluation configuration: {"Size (after training)":"1.5GB","Trainable parameters":"138M","MAdd":"154.7G"}. This record does not assert independent reproduction.

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