Marc-HealthAI/Crimson-Moon-6-DenseNet161-v2

Crimson Moon-6 v2 (DenseNet161) - Fetal Planes Classification

This model is a fine-tuned DenseNet-161 trained to classify fetal ultrasound standard planes.

Model Performance (Test Set)

• Accuracy: 0.9619
• Macro F1 Score: 0.9532
• Macro Precision: 0.9515
• Macro Recall: 0.9551

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Dataset Information

This model was trained on dataset Marc-HealthAI/fetal-planes-classification-dataset-main. Retrain with updated lr=1e-5 and effective batch size 32

• Classes: Fetal abdomen, Fetal brain, Fetal femur, Fetal thorax, Maternal cervix, Other
• Dataset Features: ['Image_name', 'Patient_num', 'Plane', 'Brain_plane', 'Operator', 'US_Machine', 'Split', 'image']

Dataset Description

No explicit description provided in dataset metadata.

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Original Dataset README Content

Click to expand original dataset README

Fetal_Planes_DB

Burgos-Artizzu, X.P., Coronado-Gutiérrez, D., Valenzuela-Alcaraz, B. et al. Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes. Sci Rep 10, 10200 (2020). https://doi.org/10.1038/s41598-020-67076-5

Data Description

A large dataset of routinely acquired maternal-fetal screening ultrasound images collected from two different hospitals by several operators and ultrasound machines. All images were manually labeled by an expert maternal fetal clinician (B.V-A.). Images were divided into 6 classes: four of the most widely used fetal anatomical planes (Abdomen, Brain, Femur and Thorax), the mother’s cervix (widely used for prematurity screening) and a general category to include any other less common image plane. Fetal brain images were further categorized into the 3 most common fetal brain planes (Trans-thalamic, Trans-cerebellum, Trans-ventricular) to judge fine grain categorization performance. The final dataset is comprised of over 12,400 images from 1,792 patients.

The dataset details are described in our open-acces paper: Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes

If you find this dataset useful, please cite:

@article{Burgos-ArtizzuFetalPlanesDataset,
  title={Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes},
  author={Burgos-Artizzu, X.P. and Coronado-Gutiérrez, D. and Valenzuela-Alcaraz, B. and Bonet-Carne, E. and Eixarch, E. and Crispi, F. and Gratacós, E.},
  journal={Nature Scientific Reports}, 
  volume={10},
  pages={10200},
  doi="10.1038/s41598-020-67076-5",
  year={2020}
}