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Automatic classification of dermatological images is an important technology that assists doctors in performing faster and more accurate classification of skin diseases. Recently, convolutional neural networks (CNNs) and Transformer networks have been employed in learning respectively the local and global features of lesion images. However, existing works mainly focus on utilizing a single neural network for feature extraction, which limits the model classification performance. In order to tackle this problem, a novel fusion model, named ConvNeXt-ST-AFF, is proposed in this paper, by combining the strengths of ConvNeXt and Swin Transformer (ConvNeXt-ST in the model’s name). In the proposed model, the pretrained ConvNeXt and Swin Transformer networks extract local and global features from images, which are then fused using Attentional Feature Fusion (AFF) submodules (AFF in the model’s name …