A Hybrid Learnable Fusion of ConvNeXt and Swin Transformer for Optimized Image Classification
Medical image classification often relies on CNNs to capture local details (e.g., lesions, nodules) or on transformers to model long-range dependencies. However, each paradigm alone is limited in addressing both fine-grained structures and broader anatomical context. We propose ConvTransGFusion, a h...
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| Main Authors: | Jaber Qezelbash-Chamak, Karen Hicklin |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | IoT |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-831X/6/2/30 |
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