# Dermatological Image Classification on ISIC 2019 (test) Source: [Dermatological Image Classification on ISIC 2019 (test)]() Collection index: [llms.txt]() Data updated: 2026-06-25T07:32:28.293Z Evaluation of dermatological image classification performance using the test split of the ISIC 2019 dataset, reporting AUC, Accuracy, and macro-averaged Precision, Recall, and F1-score. 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 | Dermatological Image Classification | | Dataset (including any stated split) | ISIC 2019 (test) | | Metric | AUC | | Unit | SCALAR | | Direction | Higher is better | | Results loaded | 5 of 5 | | 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 - [AUC](): Higher is better; unit: SCALAR. - [Accuracy](): Higher is better; unit: PERCENT. - [Macro Precision](): Higher is better; unit: PERCENT. - [Macro Recall](): Higher is better; unit: PERCENT. - [Macro F1](): Higher is better; unit: PERCENT. ## Recorded results | Method | AUC | Result date | Report paper | | --- | --- | --- | --- | | ResNet50 | 0.9057 | Not recorded | 68fa7a371e209131cea81d6f | | DenseNet121 | 0.9032 | Not recorded | 68fa7a371e209131cea81d6f | | EfficientNetV2 | 0.8796 | Not recorded | 68fa7a371e209131cea81d6f | | VAE+XGB | 0.8689 | Not recorded | 68fa7a371e209131cea81d6f | | ConvNeXt-T | 0.6644 | Not recorded | 68fa7a371e209131cea81d6f | ### ResNet50 AUC: 0.9057. Result ID: 6a3cd97efbc373592c516d15. [Reporting paper]() Recorded evaluation configuration: {"architecture\_type":"end-to-end convolutional"}. This record does not assert independent reproduction. ### DenseNet121 AUC: 0.9032. Result ID: 6a3cd97efbc373592c516d16. [Reporting paper]() Recorded evaluation configuration: {"architecture\_type":"end-to-end convolutional"}. This record does not assert independent reproduction. ### EfficientNetV2 AUC: 0.8796. Result ID: 6a3cd97efbc373592c516d13. [Reporting paper]() Recorded evaluation configuration: {"architecture\_type":"end-to-end convolutional"}. This record does not assert independent reproduction. ### VAE+XGB AUC: 0.8689. Result ID: 6a3cd97efbc373592c516d17. [Reporting paper]() [Method origin paper]() Recorded evaluation configuration: {"Representation":"256-dimensional latent","Encoder":"VAE-GAN"}. This record does not assert independent reproduction. ### ConvNeXt-T AUC: 0.6644. Result ID: 6a3cd97efbc373592c516d14. [Reporting paper]() Recorded evaluation configuration: {"architecture\_type":"end-to-end convolutional"}. This record does not assert independent reproduction. Page 1 of 1. This is an index page, not the full collection.