文献类型：期刊文献

英文题名：SKIN LESION CLASSIFICATION BASED ON RESNET-50 ENHANCED WITH ADAPTIVE SPATIAL FEATURE FUSION

作者：Liu, Runhao[1]; Chen, Ziming[2]; Zhang, Peng[3]

机构：[1] Polytechnic Institute, Zhejiang University, Hangzhou, 310015, China; [2] School of Social and Public Administration, East China University of Science and Technology, Shanghai, 200237, China; [3] School of Mathematical Sciences, Zhejiang University, Hangzhou, 310058, China

年份：2025

外文期刊名：arXiv

收录：EI(收录号：20250446675)

语种：英文

外文关键词：Classification (of information) - Computer aided diagnosis - Convolution - Convolutional neural networks - Deep learning - Dermatology - Diseases - Lung cancer - Semantics

摘要：Skin cancer classification remains a challenging problem due to high inter-class similarity, intra-class variability, and image noise in dermoscopic images. To address these issues, we propose an improved ResNet-50 model enhanced with Adaptive Spatial Feature Fusion (ASFF), which adaptively integrates multi-scale semantic and surface features to improve feature representation and reduce overfitting. The ResNet-50 model is enhanced with an adaptive feature fusion mechanism to achieve more effective multi-scale feature extraction and improve overall performance. Specifically, a dual-branch design fuses high-level semantic and mid-level detail features, which are processed through global average pooling and fully connected layers to generate adaptive weights for weighted fusion, thereby strengthening feature learning and reducing the impact of noise on classification. The method is evaluated on a subset of the ISIC 2020 dataset containing 3297 benign and malignant skin lesion images. Experimental results show that the proposed ASFF-based ResNet-50 achieves the best overall performance compared with 5 classic convolutional neural networks (CNNs) models. The proposed model reached an accuracy of 93.18% along with higher precision, recall, specificity, and F1 score. The improved model achieves an AUC value of 0.9670 and 0.9717 in the P-R and ROC curve, respectively. Then, the evaluation based on Grad-CAM further proved that the improved model adaptively focuses on lesion-relevant regions while suppressing irrelevant background information, thereby validating its enhanced feature learning capability from a deep representation perspective. These findings demonstrate that the proposed approach provides a more effective and efficient solution for computer-aided skin cancer diagnosis. Copyright ? 2025, The Authors. All rights reserved.