In the realm of skin lesion image classification, the intricate spatial and
semantic features pose significant challenges for conventional Convolutional
Neural Network (CNN)-based methodologies. These challenges are compounded by
the imbalanced nature of skin lesion datasets, which hampers the ability of
models to learn minority class features effectively. Despite augmentation
strategies, such as those using Generative Adversarial Networks (GANs),
previous attempts have not fully addressed these complexities. This study
introduces an innovative approach by integrating Graph Neural Networks (GNNs)
with Capsule Networks to enhance classification performance. GNNs, known for
their proficiency in handling graph-structured data, offer an advanced
mechanism for capturing complex patterns and relationships beyond the
capabilities of traditional CNNs. Capsule Networks further contribute by
providing superior recognition of spatial hierarchies within images. Our
research focuses on evaluating and enhancing the Tiny Pyramid Vision GNN (Tiny
Pyramid ViG) architecture by incorporating it with a Capsule Network. This
hybrid model was applied to the MNIST:HAM10000 dataset, a comprehensive skin
lesion dataset designed for benchmarking classification models. After 75 epochs
of training, our model achieved a significant accuracy improvement, reaching
89.23% and 95.52%, surpassing established benchmarks such as GoogLeNet
(83.94%), InceptionV3 (86.82%), MobileNet V3 (89.87%), EfficientNet-7B
(92.07%), ResNet18 (92.22%), ResNet34 (91.90%), ViT-Base (73.70%), and IRv2-SA
(93.47%) on the same dataset. This outcome underscores the potential of our
approach in overcoming the inherent challenges of skin lesion classification,
contributing to the advancement of image-based diagnosis in dermatology.

肤损图像分类中，复杂的空间和语义特征对传统基于卷积神经网络（CNN）的方法提出了挑战。本研究通过将图神经网络（GNNs）与胶囊网络相结合，提出了一种创新方法以增强分类性能，研究集中在评估和改进 Tiny Pyramid ViG 架构，并将其与胶囊网络相结合，结果表明该方法在克服皮肤损伤分类的潜在挑战方面具有潜力，并推动了皮肤科基于图像的诊断的发展。