Abstract
Skin cancer classification is a crucial task in medical image analysis, where precise differentiation between malignant and non-malignant lesions is essential for early diagnosis and treatment. In this study, we explore Sequential and Parallel Hybrid CNN-Transformer models with Convolutional Kolmogorov-Arnold Network (CKAN). Our approach integrates transfer learning and extensive data augmentation, where CNNs extract local spatial features, Transformers model global dependencies, and CKAN facilitates nonlinear feature fusion for improved representation learning. To assess generalization, we evaluate our models on multiple benchmark datasets (HAM10000,BCN20000 and PAD-UFES) under varying data distributions and class imbalances. Experimental results demonstrate that hybrid CNN-Transformer architectures effectively capture both spatial and contextual features, leading to improved classification performance. Additionally, the integration of CKAN enhances feature fusion through learnable activation functions, yielding more discriminative representations. Our proposed approach achieves competitive performance in skin cancer classification, demonstrating 92.81% accuracy and 92.47% F1-score on the HAM10000 dataset, 97.83% accuracy and 97.83% F1-score on the PAD-UFES dataset, and 91.17% accuracy with 91.79% F1- score on the BCN20000 dataset highlighting the effectiveness and generalizability of our model across diverse datasets. This study highlights the significance of feature representation and model design in advancing robust and accurate medical image classification.
Abstract (translated)
皮肤癌分类是医学图像分析中的关键任务，准确地区分恶性病变和非恶性病变对于早期诊断和治疗至关重要。在这项研究中，我们探讨了结合卷积柯尔莫哥洛夫-阿恩德网络（CKAN）的序列和并行混合CNN-Transformer模型。我们的方法整合了迁移学习和广泛的数据增强技术，其中CNN用于提取局部空间特征，Transformer用于建模全局依赖关系，而CKAN通过可学习的激活函数促进非线性特征融合，以改进表示学习。 为了评估泛化能力，我们使用多个基准数据集（HAM10000、BCN20000和PAD-UFES）在不同的数据分布和类别不平衡情况下对我们的模型进行评价。实验结果显示，混合CNN-Transformer架构能够有效捕捉空间特征和上下文信息，从而提高分类性能。此外，CKAN的集成通过可学习的激活函数增强了特征融合功能，生成了更具区分性的表示。 我们提出的这种方法在皮肤癌分类中表现出色，在HAM10000数据集上达到了92.81%的准确率和92.47%的F1分数，在PAD-UFES数据集上分别达到97.83%的准确率和97.83%的F1分数，以及在BCN20000数据集上实现了91.17%的准确率和91.79%的F1分数。这些结果突显了我们模型在不同数据集中具有有效性和泛化能力。 这项研究强调了特征表示和模型设计对于提高医学图像分类鲁棒性和准确性的重要性。
URL
https://arxiv.org/abs/2508.12484
