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- W4311300650 abstract "Most existing image classification methods have achieved remarkable progress in the field of natural images. However, in the data-scarce and data-complex field of diabetic foot ulcer (DFU), accurate classification of data remains a thorny problem. In this paper, we propose an end-to-end network model for the multi-class (4-class) classification task of DFU. Specifically, in order to keep the network at a certain depth and avoid the gradient disappearing during the training process, we define the identity residual block as the basic module for building the model. For the purpose of improving the expressive ability of the network, we design an asymmetric convolution module to guide the network to focus on the central region that contains more information in the image. By alternately connecting identity residual blocks with asymmetric convolution modules, our model enables the network to better extract important features of the image, while ensuring that the network does not have gradients dissipating at a certain depth. The model is validated on the DFUC2021 test set, and the F1-score and AUC value are 0.585 and 0.850, respectively, which greatly outperforms the baseline results and can provide a reference for the doctor's diagnosis." @default.
- W4311300650 created "2022-12-25" @default.
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- W4311300650 date "2022-10-21" @default.
- W4311300650 modified "2023-09-23" @default.
- W4311300650 title "Improved Residual Connection Network for Diabetic Foot Ulcers Classification" @default.
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- W4311300650 doi "https://doi.org/10.1145/3565387.3565433" @default.
- W4311300650 hasPublicationYear "2022" @default.
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