In order to solve the problem of diabetic foot auxiliary diagnosis, an efficient deep learning method with two-level ensemble convolutional neural network was proposed. This paper proposes an efficient deep learning method featured with two-level ensemble convolutional neural networks. The approach utilizes DenseNet121 and EfficientNet-B0 networks with pre-training weight as initial parameters for feature extraction during network training. The Diabetic Foot Ulcers Grand Challenge 2021 dataset is used to train the parameters of whole network, so as to realize the automatic staging of diabetic foot in terms of wound infection and ischemia. 5-fold cross-validation was used to verify the proposed trained network. The proposed method achieves high accuracy, with AUC (area under the receiver operating characteristic curve) value, accuracy, recall, precision, and F1-score of the network measured as 0.989, 0.954, 0.944, 0.954, 0.956, respectively. The method demonstrates promising potential for assisting the staging of diabetic foot in clinical.
CHEN Yuqian, LYU Donghui, SONG Anping, XIE Chuantao
. TEXAS Staging of Diabetic Foot Wounds Based on Deep Learning Approach[J]. Journal of Applied Sciences, 2024
, 42(3)
: 437
-446
.
DOI: 10.3969/j.issn.0255-8297.2024.03.006
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