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• W4200572830 abstract "Currently, artificial intelligence, especially deep learning and convolutional neural network (CNN), have been widely applied in skin lesion diagnosis. However, the limited training samples, imbalanced distribution of disease classes and similarities among different skin lesions make it difficult to accurately classify them. In this paper, we propose a transfer learning-based intelligent diagnosis mechanism for skin lesion in cloud-edge computing networks. First, we construct a cloud-edge computing model, by which users can obtain convenient and accurate diagnosis service. And then an intelligent skin lesion diagnosis mechanism is developed by combining transfer learning, model fine-tuning and data augmentation techniques, it can address the issue of low classification accuracy caused by a shortage of training samples. In addition, a modified cross-entropy loss is designed to further improve the classification accuracy when the class distribution is imbalanced in training dataset. Finally, the experimental results illustrate that our proposed mechanism achieves significant performance advantage compared with other existing methods." @default.
• W4200572830 created "2021-12-31" @default.
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• W4200572830 date "2021-10-20" @default.
• W4200572830 modified "2023-10-16" @default.
• W4200572830 title "Transfer Learning-Based Intelligent Diagnosis for Skin Lesion in Cloud-Edge Computing Networks" @default.
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• W4200572830 doi "https://doi.org/10.1109/wcsp52459.2021.9613544" @default.
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