Skin cancer recognition poses a significant challenge in the field of deep learning. While conventional convolutional neural networks have been extensively employed for classifying skin cancer images, their fixed receptive field limits their ability to capture the global features present in such images. Conversely, transformer-based models that rely on self-attention can effectively model long-range dependencies, but they come with high computational complexity and exhibit certain limitations in local feature induction. To address this issue, this paper presents a novel skin cancer recognition network named ECRNet. ECRNet has been designed to effectively capture both global and local information, and it introduces an explicit vision center to accomplish this purpose. Moreover, this paper presents a feature fusion module known as the CCPA block. This module utilizes both coordinate attention and channel attention mechanisms to extract image features and enhance the representation of skin cancer images. To evaluate the performance of ECRNet, extensive experimental comparisons were conducted on the ISIC2018 dataset. The experimental results demonstrate that ECRNet outperforms the baseline model, showing improvements of 1.19% in accuracy (ACC), 1.96% in precision, 4.08% in recall, and 3.28% in the F1 score.
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