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Medical image classification plays a crucial role in clinical treatment and medical education. However, traditional classification methods have reached performance limits and require substantial time and effort for feature extraction and selection. In this study, we introduce a novel deep learning model that combines the feature extraction part of DenseNet with the dynamic routing mechanism of capsule networks. DenseNet, known for its dense connections, aids in gradient propagation and feature reuse. Capsule networks introduce capsule layers with sensitivity to spatial relationships. By merging these two structures, our aim is to enhance the model's ability to model object hierarchy and pose variations. Our approach initially incorporates the dynamic routing mechanism of capsule networks on top of DenseNet, achieving an effective fusion of the two structures. Experimental results on the Chest X-Ray14 dataset demonstrate a significant improvement in accuracy and robustness compared to traditional DenseNet and capsule networks. To address the issue of limited annotation in medical image data, we introduce a semi-supervised learning approach and improve training balance through anti-curriculum pseudo-labeling. Furthermore, to obtain more accurate pseudo-labels, we adopt a mixed model for pseudo-label generation. This method not only effectively balances the pseudo-label predictions generated by two models but also mitigates the challenges associated with threshold-based confidence intervals in the context of multi-class, multi-label scenarios. In summary, the combination of deep convolutional neural networks and capsule networks, along with semi-supervised learning and anti-curriculum pseudo-labeling, holds great promise for medical image classification. It has significant application value in clinical practice and presents a wide-ranging prospect for the future.
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