3.1 Data collection and preprocessing

Train and validation set: The fetal MRI images employed in this investigation were sourced from the publicly accessible dataset maintained by Stanford University Digital Repository collected from Stanford Lucile Packard Children’s Hospital [35] (https://purl.stanford.edu/sf714wg0636). The dataset was accessed on 30 January 2025 from the Stanford University Digital Repository and was fully anonymized, with no access to personally identifiable information at any stage. This extensive dataset encompasses a collection of clinically relevant fetal brain MRI scans that were acquired during routine medical examinations, incorporating data from 741 patients. These scans were accompanied by corresponding gestational ages, spanning from 19 to 39 weeks, which were determined based on estimated delivery dates derived from first-trimester ultrasound measurements. Each patient case was comprised of multiple imaging planes including Axial, Coronal, and Sagittal views, thereby providing comprehensive representation of fetal brain anatomy from various perspectives. The repository contained anonymized fetal MRI data that had been collected with appropriate ethical considerations and research permissions, rendering it suitable for algorithm development and validation purposes. Importantly, this dataset comprises exclusively developmentally normal fetal brain T2-weighted MRIs acquired during routine prenatal examinations, as confirmed by the original collection protocol and used in other studies [24]. No cases with confirmed neurological anomalies were included, ensuring a focus on standard anatomical variability across gestational ages without confounding pathological features. Selection criteria prioritized diagnostic-quality scans from uncomplicated pregnancies, with exclusions applied solely for technical quality issues (e.g., severe motion artifacts, low signal-to-noise ratio, or incomplete coverage) rather than clinical pathology. This composition aligns with the study’s emphasis on automated plane classification as a foundational step for broader fetal neuroimaging workflows, including potential downstream anomaly detection. Consequently, the deep learning models were trained and validated in this study solely on normal scans, achieving robust performance for plane identification in this context. Evaluation on anomalous cases was beyond the current scope due to the dataset’s design. Future extensions of this study could incorporate anomaly-specific datasets to assess generalization.

The acquired images were subjected to systematic analysis beginning with quality assessment to ensure diagnostic adequacy. Images exhibiting severe motion artifacts, insufficient signal-to-noise ratio, or incomplete anatomical coverage were excluded from further processing to preserve dataset integrity. The remaining images were categorized according to their anatomical planes (Axial, Coronal, and Sagittal) based on visible anatomical landmarks. The plane-wise labeling was provided by the original dataset source. Overall, the dataset comprised a total of 52,561 fetal MRI images, which were distributed across three anatomical planes: 16,881 Axial view images, 16,534 Sagittal view images, and 19,146 Coronal view images. To ensure robust model evaluation, the fetal MRI dataset was organized into separate training and testing directories, with each directory containing three subdirectories corresponding to the anatomical planes (Axial, Coronal, Sagittal). This organizational structure ensures proper train-test separation and prevents data leakage during model development and evaluation.

The fetal brain MRI dataset analyzed for train and test in this study is publicly available from the Stanford University Digital Repository at https://purl.stanford.edu/sf714wg0636 [35], with no access restrictions, users can download the anonymized .jpg images (52,561 slices from 741 patient cases, 19–39 weeks GA) directly upon visiting the link. The MRI dataset used for test set is collected from OpenNeuro fetal MRI publicly available repository at fetal-fMRI-OpenNeuro. The source code for the hybrid Swin Transformer-CNN architecture and implementation used in this study is publicly available at: https://github.com/SuhaAlam/FetCAT.

Test Set: To further validate the generalizability and robustness of the proposed FetCAT model, an external test was conducted using an independent, publicly available fetal MRI dataset from OpenNeuro (accession number: ds003090) [36]. This dataset comprises resting-state BOLD fMRI scans, presenting a distinct challenge compared to the primary T2-weighted structural MRI training data. The use of BOLD fMRI data tests the model’s ability to classify anatomical planes under different contrast mechanisms and potential noise profiles. From the 173 available subjects in this repository, a single midslice was systematically extracted for each of the three fundamental anatomical planes (Axial, Coronal, and Sagittal). This process yielded a total of 519 meticulously annotated images for external validation. This test set effectively simulates a real-world scenario where a model encounters data from a different institution and acquisition protocol.

3.2 Proposed model: FetCAT – transformer-CNN cross-attention framework

To ensure reproducibility and provide a clear technical foundation for the hybrid approach, the detailed algorithmic steps and equations underlying the FetCAT framework are presented (Algorithm 1 and 2). Conceptually, a Swin Transformer and a custom AdaptiveMed-CNN are integrated in FetCAT via a cross-attention mechanism. Global contextual relationships are captured by the transformer, while local anatomical features are extracted by the CNN. These features are fused using a cross-attention module that enables focus on clinically relevant regions, thereby combining the strengths of both architectures. The overall system architecture of the proposed model is shown in Fig 2.

Algorithm 1 FetCAT: Integrated transformer-CNN cross-attention for fetal brain MRI classification.

1: Input: Dataset , , , folds K, epochs E, batch size B, learning rate η

2: Output: Model , metrics

3: Initialization

4: Initialize transformer , dim , frozen embeddings

5: Initialize AdaptiveMedCNN architecture : Conv-BN-ReLU-MaxPool layers, dim

6: Initialize cross-attention , h = 8

7: Initialize projection , fusion , classifier

8: K-Fold Training

9: for do

10:  

11:   Initialize , AdamW(), CosineAnnealingLR

12:   for do

13:    for batch do

14:     Preprocess: ,

15:     Features: ,

16:     Project:

17:     cross-attention:

18:     Fuse:

19:     Logits:

20:     Loss:

21:     Update with clipped gradients

22:    end for

23:    Evaluate on

24:    if then

25:     Save , reset patience

26:    end if

27:   end for

28:   Store

29: end for

30: Selection and Testing

31:

32: Compute across folds

33: Evaluate on for test metrics

34: Inference

35: function Predict

36:  

37:  

38:   return

39: end function

40: return

Algorithm 2 Feature fusion mechanism.

1: procedure FUSEFEATURES

2:   Project CNN features:

3:   Compute attention:

4:   

5:   

6:   

7:   

8:   

9:   Concatenate:

10:   Fuse:

11:   return f_f

12: end procedure

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Fig 2. FetCAT CNN-swin transformer architecture for fetal MRI classification.

https://doi.org/10.1371/journal.pone.0340286.g002

Transformer Module: The backbone transformer component is built upon a pre-trained Swin Transformer foundation which serves as the transformer feature extractor , originally trained on ImageNet-22k containing 14 million images with 87 million parameters [19]. The model utilizes a patch size of 4×4, generating 3,136×768 tokens. It processes input images resized to 224 × 224 in RGB format through patch embedding and hierarchical attention blocks, yielding a high-dimensional feature representation of size 1024. The Swin Transformer processes input through four sequential blocks with positional encoding, ultimately producing 1,024-dimensional feature representations through global average pooling.

AdaptiveMed-CNN Module: Parallelly, the AdaptiveMed-CNN module comprises five convolutional blocks including dilated layers and global average pooling, extracts a 512-dimensional local feature vector . The custom convolutional neural network module is designed here specifically for medical image analysis inspired by Med3D CNN model by Chen et al. [37]. Although Med3D operates on volumetric 3D data, the proposed model adapts this philosophy for 2D fetal brain MRI slices by designing a custom 2D CNN that mirrors the staged feature extraction and includes dilated convolutions to enlarge the receptive field without increasing parameter count. It comprises an initial block followed by four sequential processing blocks. The network progressively reduces spatial dimensions while increasing channel depth: from 3×224×224 input to 64×112×112, 128×56×56, 256×28×28, and finally 512×14×14. The architecture incorporates dilated convolutions in blocks 2 and 3 with dilation factor of 2 to expand the receptive field without parameter increase. Each block employs batch normalization, ReLU activation, and max pooling operations, with the final output being a 512-dimensional feature vector. This customization preserves the medical domain relevance of feature learning while remaining compatible with the available image format.

Feature Fusion Module: A learnable projection module is applied to the CNN features to align the dimensionality with transformer features, producing . These projected CNN features are then passed through a multi-head cross-attention mechanism , where Swin-derived features act as queries and the projected CNN features as keys and values. This produces attention-guided feature maps that emphasize discriminative local patterns relevant to global contexts. The cross-attention mechanism is formally defined as:

(1)

where Q represents Swin Transformer features-derived queries, K and V are CNN-derived key and value, and d_k is the key dimension. The feature fusion algorithm is described in Algorithm 2.

Following attention computation, a feature fusion module is employed. As outlined in Algorithm 2, the attended CNN features are combined with the original transformer features via concatenation, followed by a linear layer with LayerNorm and ReLU activation to produce the final fused representation . This is passed through a final classification head comprising two linear layers and a dropout layer (rate = 0.3), projecting to the output space of anatomical classes (Axial, Coronal, Sagittal). Model training is conducted using k-fold cross-validation with AdamW optimizer and cosine annealing learning rate schedule. The loss function used is cross-entropy with label smoothing. Gradient clipping is also applied to ensure stable optimization. The rationale behind this fusion was to utilize the transformer’s global contextual understanding as the primary driver for querying and weighting the most relevant local features extracted by the CNN. This configuration was proposed to ensure that the identification of salient local patterns is guided by the global structural understanding of the fetal brain.

3.3 Model variants and comparative analysis

To establish comprehensive baseline performance metrics and validate the effectiveness of the proposed hybrid architecture, several extensive comparative analysis has been conducted using multiple model categories. Initially, individual CNN architectures were evaluated, including ResNet18, VGG16, VGG19, EfficientNet, ConvNeXt, and Med3D (adapted for 2D processing). These models were employed with pre-trained weights and fine-tuned through transfer learning for the fetal brain MRI classification task. Subsequently, baseline transformer models were assessed, encompassing Swin Transfoemr [19], Vision Transformer (ViT) [20], Bidirectional Encoder representation from Image Transformers (BEiT) [21], and Data-efficient image Transformers (DEiT) [22] architectures. These transformer models were evaluated in two configurations: training from scratch with random initialization, and utilizing pre-trained weights from HuggingFace model repositories with subsequent fine-tuning.

While the primary architecture of the proposed architecture is built upon the Swin Transformer foundation due to its hierarchical feature learning and shifted window attention mechanism, comprehensive performance evaluation has been conducted with other transformers. Variations using alternative transformer architectures including Vision Transformer, BEiT, and DeiT, are combined with the proposed adaptiveMed CNN model. Additionally, the cross-attention fusion framework was evaluated using the Swin Transformer alongside pre-trained CNN backbones, such as ConvNeXt and Med3D, which exhibited enhanced performance in a comparative analysis of fetal brain MRI. Here, CNN models are initialized with pre-trained weights and subsequently fine-tuned through transfer learning before integration with the transformer components via the cross-attention mechanism. Therefore, this systematic evaluation framework enables direct performance comparison between traditional CNN approaches, baseline and pretrained transformer architectures, and the proposed hybrid cross-attention model.

3.4 Ablation study with data augmentation

Data augmentation serves as a fundamental strategy in deep learning, particularly in medical imaging, where annotated data is often scarce and expensive to acquire. First, it artificially expands the training dataset, thereby reducing overfitting and enhancing the generalization capability of the model. Second, it introduces controlled variations that simulate real-world imaging conditions, improving the model’s robustness to noise, orientation, and acquisition differences. Third, augmentation can mitigate class imbalance by generating additional samples for underrepresented categories. In the context of fetal brain MRI classification, domain-specific augmentation techniques were carefully selected to reflect anatomical variability and acquisition-induced distortions while maintaining biological plausibility. The selected methods, along with their respective parameters and clinical relevance, are detailed in Table 2. The selected augmentation techniques have been extensively utilized in recent medical imaging studies such as, Contrast Limited Adaptive Histogram Equalization (CLAHE) technique [38], data augmentation in brain tumor detection [16], augmentation for medical imaging [39] etc. However, according to multiple studies (such as in [16,17] etc.) in medical imaging, data augmentation may impair performance by introducing unrealistic distortions or masking subtle anatomical details, particularly when the dataset already possesses substantial diversity. Therefore, to evaluate the true impact of augmentation on model performance in this study, all proposed variants will be trained and assessed in both augmented and non-augmented settings.

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Table 2. Categorization of image enhancement and augmentation methods for fetal brain MRI analysis.

https://doi.org/10.1371/journal.pone.0340286.t002

3.5 Explainability analysis

To enhance clinical interpretability, explainability analysis was implemented using Gradient-weighted Class Activation Mapping (Grad-CAM) [40]. For a given input image I and target class c, the class-specific gradient is calculated as:

(2)

where represents the importance weight for feature map k with respect to class c, y^c denotes the score for class c before softmax, represents the activation at spatial location (i, j) in feature map k, and Z is the total number of pixels. The Grad-CAM heatmap is computed as:

(3)

For implementation in the proposed FetCAT architecture, Grad-CAM was applied to the final convolutional layer of the AdaptiveMed-CNN component, as this layer captures the most semantically meaningful feature representations while maintaining sufficient spatial resolution for anatomical localization. The generated heatmaps were normalized to the range [0,1] and overlaid onto the original fetal MRI images using a jet colormap with transparency parameter to ensure optimal visualization of both anatomical structures and attention regions. The steps of applying explainability with the proposed model is demonstrated in Fig 3. The explainability analysis was systematically conducted on a stratified random sample of 300 images (100 per anatomical plane) to ensure representative coverage across all classification categories. Clinical validation of the generated attention maps was performed by two expert radiologists from Combined Military Hospital, Bangladesh with specialized expertise in fetal neuroimaging, who evaluated the anatomical relevance and clinical plausibility of the highlighted regions according to established radiological interpretation protocols for fetal brain MRI plane identification.

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Fig 3. Steps of the proposed explainability method.

https://doi.org/10.1371/journal.pone.0340286.g003

As illustrated in Fig 3, the proposed pipeline incorporates explainability at two explicit stages to ensure its decisions are clinically interpretable and trustworthy. First, during the forward pass, the final convolutional block of the AdaptiveMed-CNN is retained as a high-resolution feature layer, and a hook is registered to capture both activations and gradients. This deliberate architectural choice ensures that spatially localized information required by Grad-CAM is preserved. Second, during the backward pass, class-specific gradients propagate through the cross-attention fusion module, enabling the model to reveal how transformer-derived global queries weight and select CNN-derived local features. Although Grad-CAM is a post-hoc interpretability technique, these architectural design choices ensure that the gradients and feature maps it relies on remain anatomically meaningful. The resulting visualizations appear as heatmaps that highlight critical regions such as ventricular structures for axial plane identification or the corpus callosum for coronal views. Together, these steps form an inherently explainable pathway, allowing the FetCAT model’s decisions to be traced directly to meaningful anatomical evidence rather than opaque feature embeddings, thereby providing radiologists with visual justification that aligns with their diagnostic reasoning.

3.6 Ethics statement

The fetal MRI data used for train and validation of this study is publicly available, de-identified dataset which were originally collected under an IRB-approved protocol at Stanford Lucile Packard Children’s Hospital. The test set data is also collected from the publicly available dataset (DS003090, version 1.0.0) on OpenNeuro. For both dataset, their secondary use for this research is covered by the institution’s data sharing agreement, thus not requiring separate IRB approval.

4.1 Performance analysis with model variations

The comparative performance analysis of CNN architectures are presented in Table 3 which reveals significant variations in classification accuracy across different models and augmentation strategies. The Adopted Med3D model demonstrated superior performance among CNN architectures, achieving 90.9% accuracy without augmentation and 85.9% with augmentation. This performance superiority can be attributed to Med3D’s specialized design for medical imaging tasks, incorporating domain-specific inductive biases that effectively capture medical image features relevant to anatomical plane classification. ResNet18 exhibited moderate performance with 76.79% accuracy without augmentation, while VGG architectures (VGG16: 70.21%, VGG19: 71.5%) showed relatively lower accuracy, indicating limitations in capturing complex spatial relationships inherent in fetal brain MRI planes. Notably, data augmentation consistently degraded performance across all CNN models, with accuracy reductions ranging from 1.4% (ConvNeXt) to 5.0% (Med3D).

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Table 3. Comparative performance analysis of transfer learning from CNN pretrained models.

https://doi.org/10.1371/journal.pone.0340286.t003

Table 4 demonstrates the comparative effectiveness of transformer architectures in both baseline and pretrained configurations. Baseline transformer models trained from scratch exhibited poor performance, with accuracies ranging from 52.19% (DEiT) to 66.35% (Swin), indicating insufficient training data for effective transformer parameter optimization from random initialization. However, pretrained transformer models showed substantial improvement, with Swin-Pre achieving the highest accuracy of 97.3% without augmentation, followed by ViT-Pre (96.2%), DEiT-Pre (92.1%), and BEiT-Pre (91.17%). These results underscore the critical importance of transfer learning for transformer architectures in medical imaging tasks with limited training data. The superior performance of Swin Transformer can be attributed to its hierarchical feature learning capability and shifted window attention mechanism, which effectively captures both local and global spatial relationships essential for anatomical plane identification. Similar to CNN models, data augmentation adversely affected transformer performance, with accuracy reductions observed across all pretrained models.

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Table 4. Comparative performance analysis of transformer models.

https://doi.org/10.1371/journal.pone.0340286.t004

4.1.1 Proposed FetCAT hybrid architecture analysis.

The proposed FetCAT architecture variants, detailed in Table 5, demonstrate notable performance improvements surpassing both individual CNN and transformer approaches. The Swin-AdaptiveMedCNN configuration achieved the highest accuracy of 98.64% without augmentation and 97.05% with augmentation, establishing new benchmarks for fetal brain MRI plane classification. From the results, it is observable that, although cross-attention fusion was introduced to strengthen the integration of CNN’s local spatial textures with the transformer’s global contextual embeddings, the simple cnn architecture fusion approach yielded better performance than pretrained CNN model fusion. Thus, the superior performance of AdaptiveMed-CNN fusion compared to pretrained CNN fusion (Swin-Med3D: 96.96%, Swin-ConvNext: 88.16%) is observed which can be attributed to several architectural advantages. First, the custom AdaptiveMed-CNN was specifically designed with medical imaging characteristics in mind, incorporating dilated convolutions and hierarchical feature extraction optimized for anatomical structure recognition. Second, pretrained CNN models, despite their general image understanding capabilities, may contain feature representations biased toward natural image statistics that are suboptimal for medical imaging tasks. Furthermore, alternative transformer backbones also demonstrated strong performance while aggregating them with CNN architecture : ViT-AdaptiveMedCNN (98.09%), BEiT-AdaptiveMedCNN (97.95%), and DEiT-AdaptiveMedCNN (96.37%), validating the effectiveness of the cross-attention fusion framework across different transformer architectures.

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Table 5. Comparative performance analysis of variations with proposed transformer-CNN fusion models.

https://doi.org/10.1371/journal.pone.0340286.t005

4.1.2 Statistical analysis.

The FetCAT model’s performance was rigorously evaluated through statistical analysis of three independent runs each time with 2 fold cross validations. To assess the reliability and reproducibility of the results, 95% confidence intervals (CI) was computed for all performance metrics using the t-distribution. The confidence interval for each metric was calculated as:

(4)

where is the sample mean, s is the standard deviation, n = 6 is the total number of observations (3 independent runs with 2-fold cross-validation), and t_0.025,5 = 2.571 is the critical value from the t-distribution table with 5 degrees of freedom. To evaluate model reproducibility, the coefficient of variation (CV) was computed for each metric:

(5)

As shown in Table 6, the FetCAT model achieved exceptional performance with a mean accuracy of 98.62% (95% CI: [98.48%, 98.77%]), precision of 98.62% (95% CI: [98.48%, 98.77%]), recall of 98.62% (95% CI: [98.48%, 98.77%]), and F1-score of 98.62% (95% CI: [98.48%, 98.77%]). All classification metrics demonstrated CV values below 0.15%, indicating excellent reproducibility and stability across multiple runs. The narrow confidence interval widths (0.29% for all classification metrics) further confirm the model’s consistent performance. These statistical measures demonstrate that the FetCAT model produces highly reliable and reproducible results, making it suitable for deployment in clinical applications.

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Table 6. Summary statistics and 95% confidence intervals for proposed FetCAT model performance metrics.

https://doi.org/10.1371/journal.pone.0340286.t006

From Table 7, it can be observed that the proposed FetCAT model achieves strong performance in classifying fetal ultrasound planes, with class-wise point estimates for accuracy exceeding 97.5% across Axial (98.6%; 95% CI: 0.979–0.993), Coronal (97.5%; 95% CI: 0.962–0.988), and Sagittal (99.1%; 95% CI: 0.985–0.997) views. Here, the point estimate represents the single best approximation of the true metric from the sample data (e.g., 98.6% for Axial accuracy), while the 95% confidence interval (CI) provides a range of plausible values for the population parameter, indicating that 95% of repeated studies would capture the true value within bounds like 0.979–0.993, thus quantifying estimate reliability. Precision point estimates are consistently high, particularly for Axial (99.1%; 95% CI: 0.985–0.997) and Sagittal (98.9%; 95% CI: 0.983–0.995), reflecting low false positive rates within their respective confidence intervals. Recall point estimates align closely with accuracies, demonstrating reliable detection of true instances for each plane, though Coronal sensitivity is slightly lower at 97.5% (95% CI: 0.962–0.988). F1-score point estimates, balancing precision and recall, range from 97.7% for Coronal (95% CI: 0.964–0.990) to 99.0% for Sagittal (95% CI: 0.984–0.996), indicating balanced effectiveness supported by narrow confidence intervals for clinical use in automated fetal plane identification. The confusion matrix of 2 folds validation and the test set is illustrated in Fig 4.

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Fig 4. Confusion matrices for plane classification using FetCAT model.

(a) Fold 1 validation. (b) Fold 2 validation. (c) Test set.

https://doi.org/10.1371/journal.pone.0340286.g004

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Table 7. Class-wise performance metrics for fetal plane classification using proposed FetCAT model.

https://doi.org/10.1371/journal.pone.0340286.t007

Fig 5 illustrates the epoch-wise training progression for proposed model variations, demonstrating rapid convergence characteristics across all hybrid architectures. The Swin-AdaptiveMedCNN configuration exhibited the most stable convergence profile, reaching optimal performance within minimal oscillation. Training loss curves demonstrate consistent monotonic decrease without significant overfitting indicators, suggesting effective regularization through the cross-attention mechanism and dropout layers. The comparative accuracy visualization in Fig 6 clearly delineates performance hierarchies across model categories. Individual CNN and baseline transformer models cluster in the lower performance range (50-70%), pretrained transformers achieve intermediate performance (90-97%), while the proposed FetCAT variants consistently occupy the highest performance tier (96-98.6%).

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Fig 5. Training convergence analysis showing average epoch-wise accuracy and loss progression for proposed model variations.

https://doi.org/10.1371/journal.pone.0340286.g005

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Fig 6. Visual representation of comparative accuracy analysis between cnn, transformer and proposed model variations.

https://doi.org/10.1371/journal.pone.0340286.g006

The proposed FetCAT model also demonstrated excellent calibration, achieving a low Expected Calibration Error (ECE) of 0.0274 and a Brier Score of 0.0556 (Fig 7). The demonstrated low ECE of 0.0274 is a critical indicator that the model’s predicted probabilities are highly trustworthy. Also, the Brier score quantifies the mean squared difference between a model’s predicted probabilities and actual binary outcomes, serving as a comprehensive metric for probabilistic forecast accuracy where lower values (closer to 0) indicate better calibration and sharpness, with a score of 0.0556 suggesting excellent performance. Moreover, the binned reliability diagram (right) reinforces this, with observed accuracy (blue bars) closely tracking average confidence (orange bars) in each bin.

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Fig 7. Average calibration and reliability plots of the proposed model.

https://doi.org/10.1371/journal.pone.0340286.g007

4.1.3 Generalization performance on test set.

Table 8 presents the comprehensive performance evaluation on the OpenNeuro MRI test dataset, demonstrating the proposed FetCAT model’s superior performance with an accuracy of 81.0%, significantly outperforming Swin Transformer (65.1%), Vision Transformer (59.5%), and VGG19 (44.0%). Critically, McNemar’s test confirmed that the performance difference between FetCAT and every baseline model is statistically significant (p < 0.001 for all comparisons), with the values increasing as the performance of the baselines decreased. This trend is clearly observed, from Swin Transformer (Acc: 65.1%, ) and Vision Transformer (Acc: 59.5%, ) down to the substantially weaker VGG19 (Acc: 44.0%, ). These results demonstrate that FetCAT is not only empirically superior but also statistically more robust and reliable when classifying fetal MRI planes from an unseen dataset with a different acquisition protocol, highlighting its strong potential for real-world clinical deployment.

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Table 8. Model performances with statistical comparisons using test set data (OpenNeuro MRI).

https://doi.org/10.1371/journal.pone.0340286.t008

4.2 Explainability analysis

The Grad-CAM visualization results presented in Fig 8 provide critical insights into the model’s decision-making process for anatomical plane classification. For Axial plane images, the model consistently focused on ventricular structures and basal ganglia regions, which represent key anatomical landmarks for Axial plane identification. Coronal plane classifications demonstrated attention to corpus callosum and anterior-posterior brain structures, while Sagittal plane focus concentrated on cerebellar and brainstem regions. These attention patterns align with clinical expertise for manual plane identification, demonstrating that the model has learned clinically relevant anatomical features rather than spurious correlations. The explainability analysis validates the clinical applicability of the proposed approach by confirming that automated classifications are based on anatomically meaningful regions consistent with radiological interpretation protocols. This interpretability is crucial for clinical adoption and provides confidence in the model’s diagnostic reasoning process. The explainability analysis was validated through expert evaluation of 300 randomly selected images by two expert radiologists from Combined Military Hospital, Bangladesh who confirmed the anatomical relevance of the highlighted regions.

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Fig 8. Explainability results with heatmap on three fetal brain MRI samples highlighting key anatomical regions.

https://doi.org/10.1371/journal.pone.0340286.g008

4.2.1 Clinical framing.

Fig 9 illustrates the transformative impact of integrating the FetCAT model into the clinical workflow for fetal brain MRI assessment. The conventional pathway relies on the manual, time-intensive process of slice-by-slice identification by a neuroradiologist, which is highly dependent to inter-observer variability, high cognitive load, and the confounding effects of motion artifacts, often leading to diagnostic delays and inconsistencies. In contrast, the AI-assisted pathway demonstrates a streamlined workflow where FetCAT performs automated, initial plane classification, drastically reducing pre-diagnostic sorting time. The radiologist then transitions to a validating role, efficiently reviewing the AI-sorted outputs with the aid of Grad-CAM explanations that highlight clinically relevant anatomical regions. This synergistic human-AI collaboration mitigates the traditional bottlenecks, resulting in a verified, expedited assessment with enhanced diagnostic confidence and a more reliable, standardized process.

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Fig 9. Traditional vs. FetCAT assisted transformative workflow in clinical practice.

https://doi.org/10.1371/journal.pone.0340286.g009

4.3 Ablation study analysis

The systematic ablation analysis from Tables 3, 4 and 5 as well as visual representation in Figure 6 with and without data augmentation revealed consistent performance degradation across all model configurations when data augmentation was applied. This contradicts conventional expectations in deep learning applications. This counterintuitive finding can be attributed to the substantial dataset size of 52,561 fetal brain MRI images, which already encompasses extensive natural variations across gestational ages (19-39 weeks), anatomical morphologies, imaging conditions, and fetal positioning that eliminate the necessity for synthetic data expansion. The comprehensive organic diversity present in the motion-degraded dataset renders artificial augmentation redundant, as transformations introduce synthetic variations that overlap with naturally occurring patterns while risking the generation of clinically implausible image modifications. Furthermore, fetal brain MRI classification relies on detecting subtle anatomical landmarks and tissue boundaries critical for plane identification, and augmentation techniques may inadvertently distort these delicate morphological features essential for accurate diagnosis. Geometric transformations such as rotation and scaling can alter spatial relationships between anatomical structures, while intensity modifications may compromise tissue contrast patterns that radiologists depend upon for manual interpretation, aligning with recent findings demonstrating limited efficacy or adverse effects of traditional augmentation strategies in specialized medical imaging domains [41].

A more focused per-augmentation ablation study was performed to assess the impact of different data augmentation strategies on fetal brain plane classification. For each augmentation type: geometric, intensity-based, and noise/deformation perturbations, an additional 25,000 augmented images were generated and combined with the original dataset, resulting in a total of 77,565 training samples per experiment. The results are shown in Table 9. Despite this substantial increase in data volume, the three individual augmentation groups yielded similar performance, each achieving an average validation accuracy of approximately 0.962. When all augmentation techniques were applied jointly, the model demonstrated a moderate improvement, reaching an average accuracy of 0.9706. Notably, the model trained without any augmentations achieved the highest performance (0.9864 accuracy), indicating that the original dataset already provided sufficient variability and that augmentation did not consistently enhance generalization. These results suggest that, for this fetal MRI plane classification task, augmentation offers limited benefit and the baseline model is inherently robust.

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Table 9. Ablation study results with the proposed FetCAT model across different augmentation strategies.

https://doi.org/10.1371/journal.pone.0340286.t009

Overall, the comprehensive evaluation demonstrates that the FetCAT hybrid architecture effectively combines the complementary strengths of CNN local feature extraction and transformer global contextual modeling, achieving state-of-the-art performance in motion-degraded fetal brain MRI plane classification while maintaining clinical interpretability through explainable AI mechanisms.