Plantar pressure image classification employing residual-network model-based conditional generative adversarial networks: a comparison of normal, planus, and talipes equinovarus feet
| Autor: | Zairan Li, Rubén González Crespo, Nilanjan Dey, Dan Wang, Fuqian Shi, Jianlin Han |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Pixel
Contextual image classification Artificial neural network Computer science business.industry Deep learning Feature extraction Pattern recognition Convolutional neural network Theoretical Computer Science Convolution conditional generative adversarial network deep neural networks JCR resident network plantar pressure Scopus Geometry and Topology Artificial intelligence business Software Network model image classification |
| Zdroj: | Re-Unir. Archivo Institucional de la Universidad Internacional de La Rioja instname |
| Popis: | The number of deep learning (DL) layers increases, and following the performance of computing nodes improvement, the output accuracy of deep neural networks (DNN) faces a bottleneck problem. The resident network (RN) based DNN model was applied to address these issues recently. This paper improved the RN and developed a rectified linear unit (ReLU) based conditional generative adversarial nets (cGAN) to classify plantar pressure images. A foot scan system collected the plantar pressure images, in which normal (N), planus (PL), and talipes equinovarus feet (TE) data-sets were acquired subsequently. The 9-foot types named N, PL, TE, N-PL, N-TE, PL-N, PL-TE, TE-N, and TE-PL were classified using the proposed DNN models, named resident network-based conditional generative adversarial nets (RNcGAN). It improved the RN structure firstly and the cGAN system hereafter. In the classification of plantar pressure images, the pixel-level state matrix can be direct as an input, different from the previous image classification task with image reduction and feature extraction. cGAN can directly output the pixels of the image without any simplification. Finally, the model achieved better results in the evaluation indicators of accuracy (AC), sensitivity (SE), and F1-measurement (F1) by comparing to artificial neural networks (ANN), k-nearest neighbor (kNN), Fast Region-based Convolution Neural Network (Fast R-CNN), visual geometry group (VGG16), scaled-conjugate-gradient convolution neural networks (SCG-CNN), GoogleNet, AlexNet, ResNet-50–177, and Inception-v3. The final prediction of class accuracy is 95.17%. Foot type classification is vital for producing comfortable shoes in the industry. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. |
| Databáze: | OpenAIRE |
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