Multimodal image fusion for ich detection and classification using parallel Dl models
Intracranial haemorrhage (ICH) can be a potential consequence of traumatic brain injuries. Single-modality images provide limited information due to different imaging principles and organ structures. Multimodality image fusion techniques can help incorporate different types of images into a single,...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21681163.2025.2468436 |
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| author | Sri Sangepu Nagaraju S. Prince Mary V. Pavani Chandra Nandam Gayatri |
| author_facet | Sri Sangepu Nagaraju S. Prince Mary V. Pavani Chandra Nandam Gayatri |
| author_sort | Sri Sangepu Nagaraju |
| collection | DOAJ |
| description | Intracranial haemorrhage (ICH) can be a potential consequence of traumatic brain injuries. Single-modality images provide limited information due to different imaging principles and organ structures. Multimodality image fusion techniques can help incorporate different types of images into a single, high-quality image, but current methods face challenges like fusion artefacts, complex design, and high computational costs. This paper introduces an innovative multimodal medical image fusion method using parallel Deep Learning (DL) models. The research uses a carefully chosen multimodal dataset, including CT and MRI, and a novel segmentation algorithm called DFA-UNet. The model employs an encoder-decoder structure and uses a DFA attention module for efficient image feature extraction and easy channel feature weight integration. The method uses Graph Neural Networks (GNNs) for spatial features and Recurrent Neural Networks (RNNs) for temporal complexities. The architecture starts with pre-processing to standardize and enhance multimodal data, and simultaneous DL models are included to analyze fused input for a comprehensive understanding of ICH patterns. Attention mechanisms are carefully included to improve accessibility. The proposed parallel DL architecture demonstrates high efficiency in classifying images as either haemorrhage or non-haemorrhage. |
| format | Article |
| id | doaj-art-860c507a6daf479e90c8aa6eed787832 |
| institution | DOAJ |
| issn | 2168-1163 2168-1171 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization |
| spelling | doaj-art-860c507a6daf479e90c8aa6eed7878322025-08-20T03:05:30ZengTaylor & Francis GroupComputer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization2168-11632168-11712025-12-0113110.1080/21681163.2025.2468436Multimodal image fusion for ich detection and classification using parallel Dl modelsSri Sangepu Nagaraju0S. Prince Mary1V. Pavani Chandra2Nandam Gayatri3Department of Computer Science, JNTU Hyderabad, Hyderabad, Telangana, IndiaDepartment of Computer Science, Sathyabama University of Chennai India, Chennai, Tamil Nadu, IndiaDepartment of Computer Science, St. Ann’s Degree college Osmania University Hyderabad, Hyderabad, Telangana, IndiaDepartment of Computer Science, Vel Tech Engineering College, Chennai, Tamilnadu, IndiaIntracranial haemorrhage (ICH) can be a potential consequence of traumatic brain injuries. Single-modality images provide limited information due to different imaging principles and organ structures. Multimodality image fusion techniques can help incorporate different types of images into a single, high-quality image, but current methods face challenges like fusion artefacts, complex design, and high computational costs. This paper introduces an innovative multimodal medical image fusion method using parallel Deep Learning (DL) models. The research uses a carefully chosen multimodal dataset, including CT and MRI, and a novel segmentation algorithm called DFA-UNet. The model employs an encoder-decoder structure and uses a DFA attention module for efficient image feature extraction and easy channel feature weight integration. The method uses Graph Neural Networks (GNNs) for spatial features and Recurrent Neural Networks (RNNs) for temporal complexities. The architecture starts with pre-processing to standardize and enhance multimodal data, and simultaneous DL models are included to analyze fused input for a comprehensive understanding of ICH patterns. Attention mechanisms are carefully included to improve accessibility. The proposed parallel DL architecture demonstrates high efficiency in classifying images as either haemorrhage or non-haemorrhage.https://www.tandfonline.com/doi/10.1080/21681163.2025.2468436Intracranial haemorrhage classificationmultimodality fusionhaematoma segmentationdeep learninggraph neural network |
| spellingShingle | Sri Sangepu Nagaraju S. Prince Mary V. Pavani Chandra Nandam Gayatri Multimodal image fusion for ich detection and classification using parallel Dl models Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization Intracranial haemorrhage classification multimodality fusion haematoma segmentation deep learning graph neural network |
| title | Multimodal image fusion for ich detection and classification using parallel Dl models |
| title_full | Multimodal image fusion for ich detection and classification using parallel Dl models |
| title_fullStr | Multimodal image fusion for ich detection and classification using parallel Dl models |
| title_full_unstemmed | Multimodal image fusion for ich detection and classification using parallel Dl models |
| title_short | Multimodal image fusion for ich detection and classification using parallel Dl models |
| title_sort | multimodal image fusion for ich detection and classification using parallel dl models |
| topic | Intracranial haemorrhage classification multimodality fusion haematoma segmentation deep learning graph neural network |
| url | https://www.tandfonline.com/doi/10.1080/21681163.2025.2468436 |
| work_keys_str_mv | AT srisangepunagaraju multimodalimagefusionforichdetectionandclassificationusingparalleldlmodels AT sprincemary multimodalimagefusionforichdetectionandclassificationusingparalleldlmodels AT vpavanichandra multimodalimagefusionforichdetectionandclassificationusingparalleldlmodels AT nandamgayatri multimodalimagefusionforichdetectionandclassificationusingparalleldlmodels |