Skin cancer is a major concern to public health, accounting for one-third of
the reported cancers. If not detected early, the cancer has the potential for
severe consequences. Recognizing the critical need for effective skin cancer
classification, we address the limitations of existing models, which are often
too large to deploy in areas with limited computational resources. In response,
we present a knowledge distillation based approach for creating a lightweight
yet high-performing classifier. The proposed solution involves fusing three
models, namely ResNet152V2, ConvNeXtBase, and ViT Base, to create an effective
teacher model. The teacher model is then employed to guide a lightweight
student model of size 2.03 MB. This student model is further compressed to
469.77 KB using 16-bit quantization, enabling smooth incorporation into edge
devices. With six-stage image preprocessing, data augmentation, and a rigorous
ablation study, the model achieves an impressive accuracy of 98.75% on the
HAM10000 dataset and 98.94% on the Kaggle dataset in classifying benign and
malignant skin cancers. With its high accuracy and compact size, our model
appears to be a potential choice for accurate skin cancer classification,
particularly in resource-constrained settings.

我们提出基于知识蒸馏的方法来创建一个轻量级且高性能的皮肤癌分类器，通过融合 ResNet152V2、ConvNeXtBase 和 ViT Base 等三个模型来创建一个有效的教师模型，进而进行指导以得到一个大小为 2.03 MB 的轻量级学生模型，通过 16 位量化将其进一步压缩到 469.77 KB，以实现边缘设备的平滑整合，在六个阶段的图像预处理、数据增强以及严谨的消融研究的基础上，在 HAM10000 数据集上取得了 98.75% 的准确率，在 Kaggle 数据集上取得了 98.94% 的准确率，在资源受限的环境中，我们的模型以其高准确率和紧凑的大小成为一个潜在的准确的皮肤癌分类选择。