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Multi-Scale Feature Integrated Dual-Channel Attention Routing Capsule Networks Design for Enhanced Skin Cancer Classification
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DOI：10.1002/ima.70401.png)
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Abstract
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Timely diagnosis and treatment of skin cancer is crucial for improving patient prognosis. Convolutional neural networks (CNNs) have been applied to skin cancer image classification, but they are difficult to fully capture feature pose information. To address it, a dual-channel Attention Routing capsule network (DC-AR-CapsNet) is established to take advantage of capsule networks for skin cancer image classification. To overcome the limitations associated with inadequate feature extraction capabilities, a hybrid domain feature-weighted DCS module is proposed. By combining with multi-scale feature fusion, a two-branch parallel architecture is established to enhance the ability of capsule network in capturing features from skin cancer images. Then a self-attention mechanism is incorporated to reduce redundancy of the primary capsules and inhibit the homogenization of capsule features by calculating the cosine similarity values of capsules in the same layer. Furthermore, a dynamic loss function is constructed to adaptively adjust the decision boundary based on the classification outcomes. Experimental results indicate that the designed DC-AR-CapsNet model achieves superior image classification accuracy, despite the increase in the number of parameters. It outperforms existing models and achieves excellent accuracy and robustness in skin lesion classification.
Keywords:
capsule network
classification
CNN
loss function
skin cancer
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