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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Hlavní autoři:	Jaber Qezelbash-Chamak, Karen Hicklin
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	MDPI AG 2025-05-01
Edice:	IoT
Témata:	machine learning deep learning ConvNeXt Swin Transformer feature fusion biomedical informatics
On-line přístup:	https://www.mdpi.com/2624-831X/6/2/30
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