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This is a Human Clinical Trial study.

September 10, 2025Open Access

Improved pulmonary embolism detection in CT pulmonary angiogram scans with hybrid vision transformers and deep learning techniques

Key Points

Our method achieves a remarkable accuracy of 97.80%, significantly enhancing early pulmonary embolism detection.
This ensemble framework leverages the strengths of resnet50, densenet121, and swin transformer for improved model robustness.
A sophisticated preprocessing pipeline using autoencoders and data augmentation addresses challenges in medical imaging datasets.
The results indicate that this hybrid approach outperforms existing pre-trained models, highlighting its impact on clinical decision-making.

Abstract

Abstract Pulmonary embolism (PE) represents a severe, life-threatening cardiovascular condition and is notably the third leading cause of cardiovascular mortality, after myocardial infarction and stroke. This pathology occurs when blood clots obstruct the pulmonary arteries, impeding blood flow and oxygen exchange in the lungs. Prompt and accurate detection of PE is critical for appropriate clinical decision-making and patient survival. The complexity involved in interpreting medical images can often results misdiagnosis. However, recent advances in Deep Learning (DL) have substantially improved the capabilities of Computer-Aided Diagnosis (CAD) systems. Despite these advancements, existing single-model DL methods are limited when handling complex, diverse, and imbalanced medical imaging datasets. Addressing this gap, our research proposes an ensemble framework for classifying PE, capitalizing on the unique capabilities of ResNet50, DenseNet121, and Swin Transformer models. This ensemble method harnesses the complementary strengths of convolutional neural networks (CNNs) and vision transformers (ViTs), leading to improved prediction accuracy and model robustness. The proposed methodology includes a sophisticated preprocessing pipeline leveraging autoencoder (AE)-based dimensionality reduction, data augmentation to avoid overfitting, discrete wavelet transform (DWT) for multiscale feature extraction, and Sobel filtering for effective edge detection and noise reduction. The proposed model was rigorously evaluated using the public Radiological Society of North America (RSNA-STR) PE dataset, demonstrating remarkable performance metrics of 97.80% accuracy and a 0.99 for Area Under Receiver Operating Curve (AUROC). Comparative analysis demonstrated superior performance over state-of-the-art pre-trained models and recent ViT-based approaches, highlighting our method’s effectiveness in improving early PE detection and providing robust support for clinical decision-making.

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Cite This Study

Abdelhamid et al. (Tue,) studied this question.

synapsesocial.com/papers/68c1d23054b1d3bfb60f7e11 https://doi.org/https://doi.org/10.1038/s41598-025-16238-4

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