Synergistic learnable frequency and multi-scale spatial network for lightweight skin cancer classification_Journal of Biomedical Engineering

Authors：

 ZHUANG Jianjun ^1,2 , LYU Zhenglong ¹ , LI Xiang ¹

1. School of Electronics and Information Engineering, Anhui Jianzhu University, Hefei 230601, P. R. China;
2. Zhongda Hospital Institute of Smart Healthcare, Jiangsu Provincial Key Laboratory of Intelligent Medical Image Computing, School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China;

Corresponding?author：

ZHUANG Jianjun, Email: jjzhuang@nuist.edu.cn

Keywords：

Lightweight classification; Learnable frequency enhancement; Multi-scale aggregation; Directional spatial calibration; Auxiliary diagnosis

DOI：

10.7507/1001-5515.202604067

Video：

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Abstract Full text Figures/Tables Video References Cited by

Early screening of skin cancer is crucial to the survival rate of patients. Although deep learning has made significant progress in dermoscopic image analysis, the blurred edge of the lesion, the vulnerability to noise interference, and the limited computing resources at the time of model deployment are still the main bottlenecks. To this end, this paper proposes a frequency-space collaborative enhancement network (FSC-Net) based on lightweight classification. Aiming at the problem of blurred lesion edge and noise interference, the network first constructs a learning frequency enhancement module. Through the dynamic selective enhancement of frequency domain features, the lesion edge is finely characterized while suppressing high-frequency artifacts. Secondly, aiming at the scale heterogeneity of lesion morphology, this paper proposes a multi-scale aggregation module, which uses multi-branch pooling to reduce the loss of deep semantic features in the lightweight network. Finally, in order to solve the problem of difficult localization of complex lesion areas, this paper introduces a directional spatial calibration mechanism, which realizes accurate localization of lesion features through orthogonal decoupling coding and asymmetry factors. The experimental results on the 2019 international skin image collaboration challenge (ISIC2019) and the human against machine with 10000 training images (HAM10000) dataset show that FSC-Net achieves 93.41% eight-classification accuracy and 95.84% seven-classification accuracy with a lower number of parameters. Compared with the existing advanced models, the proposed method achieves a better balance between computational overhead and diagnostic performance, and provides a robust and efficient solution for auxiliary diagnosis in resource-constrained environments.

Citation： ZHUANG Jianjun, LYU Zhenglong, LI Xiang. Synergistic learnable frequency and multi-scale spatial network for lightweight skin cancer classification. Journal of Biomedical Engineering, 2026, 43(4): 836-844. doi: 10.7507/1001-5515.202604067 Copy

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Journal of Biomedical Engineering

Synergistic learnable frequency and multi-scale spatial network for lightweight skin cancer classification

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