Table 1.
Overview of automated fetal brain MRI analysis studies.
Fig 1.
Methodological framework.
Fig 2.
FetCAT CNN-swin transformer architecture for fetal MRI classification.
Table 2.
Categorization of image enhancement and augmentation methods for fetal brain MRI analysis.
Fig 3.
Steps of the proposed explainability method.
Table 3.
Comparative performance analysis of transfer learning from CNN pretrained models.
Table 4.
Comparative performance analysis of transformer models.
Table 5.
Comparative performance analysis of variations with proposed transformer-CNN fusion models.
Table 6.
Summary statistics and 95% confidence intervals for proposed FetCAT model performance metrics.
Fig 4.
Confusion matrices for plane classification using FetCAT model.
(a) Fold 1 validation. (b) Fold 2 validation. (c) Test set.
Table 7.
Class-wise performance metrics for fetal plane classification using proposed FetCAT model.
Fig 5.
Training convergence analysis showing average epoch-wise accuracy and loss progression for proposed model variations.
Fig 6.
Visual representation of comparative accuracy analysis between cnn, transformer and proposed model variations.
Fig 7.
Average calibration and reliability plots of the proposed model.
Table 8.
Model performances with statistical comparisons using test set data (OpenNeuro MRI).
Fig 8.
Explainability results with heatmap on three fetal brain MRI samples highlighting key anatomical regions.
Fig 9.
Traditional vs. FetCAT assisted transformative workflow in clinical practice.
Table 9.
Ablation study results with the proposed FetCAT model across different augmentation strategies.