Placenta Accreta Spectrum Detection Using an MRI-based Hybrid CNN-Transformer Model
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911329614299136 |
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| author | Ali, Sumaiya Alhothali, Areej Alzamzami, Ohoud Albasri, Sameera Abduljabbar, Ahmed Alwazzan, Muhammad |
| author_facet | Ali, Sumaiya Alhothali, Areej Alzamzami, Ohoud Albasri, Sameera Abduljabbar, Ahmed Alwazzan, Muhammad |
| contents | Placenta Accreta Spectrum (PAS) is a serious obstetric condition that can be challenging to diagnose with Magnetic Resonance Imaging (MRI) due to variability in radiologists' interpretations. To overcome this challenge, a hybrid 3D deep learning model for automated PAS detection from volumetric MRI scans is proposed in this study. The model integrates a 3D DenseNet121 to capture local features and a 3D Vision Transformer (ViT) to model global spatial context. It was developed and evaluated on a retrospective dataset of 1,133 MRI volumes. Multiple 3D deep learning architectures were also evaluated for comparison. On an independent test set, the DenseNet121-ViT model achieved the highest performance with a five-run average accuracy of 84.3%. These results highlight the strength of hybrid CNN-Transformer models as a computer-aided diagnosis tool. The model's performance demonstrates a clear potential to assist radiologists by providing a robust decision support to improve diagnostic consistency across interpretations, and ultimately enhance the accuracy and timeliness of PAS diagnosis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_18573 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Placenta Accreta Spectrum Detection Using an MRI-based Hybrid CNN-Transformer Model Ali, Sumaiya Alhothali, Areej Alzamzami, Ohoud Albasri, Sameera Abduljabbar, Ahmed Alwazzan, Muhammad Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Placenta Accreta Spectrum (PAS) is a serious obstetric condition that can be challenging to diagnose with Magnetic Resonance Imaging (MRI) due to variability in radiologists' interpretations. To overcome this challenge, a hybrid 3D deep learning model for automated PAS detection from volumetric MRI scans is proposed in this study. The model integrates a 3D DenseNet121 to capture local features and a 3D Vision Transformer (ViT) to model global spatial context. It was developed and evaluated on a retrospective dataset of 1,133 MRI volumes. Multiple 3D deep learning architectures were also evaluated for comparison. On an independent test set, the DenseNet121-ViT model achieved the highest performance with a five-run average accuracy of 84.3%. These results highlight the strength of hybrid CNN-Transformer models as a computer-aided diagnosis tool. The model's performance demonstrates a clear potential to assist radiologists by providing a robust decision support to improve diagnostic consistency across interpretations, and ultimately enhance the accuracy and timeliness of PAS diagnosis. |
| title | Placenta Accreta Spectrum Detection Using an MRI-based Hybrid CNN-Transformer Model |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2512.18573 |