Marc-HealthAI/Crimson-Flame-6-resNet18

Crimson Flame-6 (ResNet18) - Fetal Planes Classification

This model is a fine-tuned ResNet-18 trained to classify fetal ultrasound standard planes.

Model Performance (Test Set)

• Accuracy: 0.9438
• Macro F1 Score: 0.9332
• Macro Precision: 0.9259
• Macro Recall: 0.9430

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Training History

Epoch	Eval Loss	Eval Accuracy	Eval Precision (Macro)	Eval Recall (Macro)	Eval F1 (Macro)
1.0	0.2145	0.9184	0.8942	0.9394	0.9130
2.0	0.1825	0.9341	0.9122	0.9455	0.9271
3.0	0.1793	0.9301	0.9137	0.9438	0.9258
4.0	0.1710	0.9325	0.9174	0.9458	0.9290
5.0	0.1618	0.9337	0.9190	0.9446	0.9307
6.0	0.1672	0.9418	0.9280	0.9493	0.9377
7.0	0.1711	0.9414	0.9238	0.9566	0.9381
8.0	0.2063	0.9503	0.9466	0.9432	0.9431
9.0	0.1635	0.9406	0.9223	0.9525	0.9359
10.0	0.2121	0.9366	0.9194	0.9471	0.9319
11.0	0.1645	0.9572	0.9491	0.9572	0.9530
12.0	0.1618	0.9531	0.9375	0.9579	0.9471
13.0	0.1864	0.9499	0.9339	0.9527	0.9426
14.0	0.1858	0.9515	0.9360	0.9555	0.9451
15.0	0.2731	0.9531	0.9530	0.9484	0.9501
16.0	0.2352	0.9523	0.9420	0.9520	0.9467
17.0	0.1957	0.9463	0.9297	0.9545	0.9411

Dataset Information

This model was trained on dataset Marc-HealthAI/fetal-planes-classification-dataset-main.

• 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}
}