Skin lesion recognition via global-local attention and dual-branch input network

Owing to the deficiency of training data, the variety of skin lesion shapes for different patients, inter-class similarity, and intra-class variation, the recognition accuracy and speed in skin lesion recognition based on deep learning remain challenging. To solve the above issues, we propose a Dual-Branch and Global-Local Attention network based on ResNet50 (DGLA-ResNet50) to reduce model parameters and improve identification accuracy. For inter-class and intra-class problems, a global-local attention mechanism is designed to obtain the dependency information of query points in horizontal and vertical directions in turn for obtaining the global information indirectly. And we choose to obtain the local information through multiple convolutions simultaneously. Aiming at insufficient samples, the attention mechanism is lightened from the perspectives of reducing query points and the points associated with them, so as to reduce parameter redundancy. In addition, we present a dual-branch input network to deal with the problem of image variety, in which two branches are used to extract image features with different resolutions for fusion, so as to expand the network receptive field. We evaluated DGLA-ResNet50 on ISIC2018 and ISIC2019. The experimental results on ISIC2018 demonstrate that DGLA-ResNet50 has a recognition accuracy of 90.71%, while the parameters of the model are only 104.2M and the FLOPs value is 15.6G. Compared with the common models, our model obtains significantly better performance. The results indicate that DGLA-ResNet50 can improve the accuracy well while ensuring the lightweight of the model, and can prospectively assist doctors in the rapid diagnosis of skin lesions.