Abstract
Chronic wounds have a long recovery time, occur extensively, and are difficult to treat. They cause not only great suffering to many patients but also bring enormous work burden to hospitals and doctors. Therefore, an automated chronic wound detection method can efficiently assist doctors in diagnosis, or help patients with initial diagnosis, reduce the workload of doctors and the treatment costs of patients. In recent years, due to the rise of big data, machine learning methods have been applied to Image Identification, and the accuracy of the result has surpassed that of traditional methods. With the fully convolutional neural network proposed, image segmentation and target detection have also achieved excellent results. However, due to the protection of patient privacy, medical images are often difficult to obtain and insufficient training data leads to poor segmentation and recognition. To solve the above problem, we propose the chronic wounds image generator based on DCGANs. First, we select high-quality images of chronic wounds and process them to form a data set containing 520 images. Then we build a generator and discriminator network model to generate new images. Finally, we use the existing methods of chronic wound segmentation and recognition to test. The results show that the generated images can be used to expand the training set and further improve the segmentation and recognition accuracy.
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Acknowledgements
This work was supported by the National Key R&D Program of China 2018YFB1003203 and the National Natural Science Foundation of China (Grant No. 61672528).
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Zhang, J., Zhu, E., Guo, X., Chen, H., Yin, J. (2018). Chronic Wounds Image Generator Based on Deep Convolutional Generative Adversarial Networks. In: Li, L., Lu, P., He, K. (eds) Theoretical Computer Science. NCTCS 2018. Communications in Computer and Information Science, vol 882. Springer, Singapore. https://doi.org/10.1007/978-981-13-2712-4_11
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DOI: https://doi.org/10.1007/978-981-13-2712-4_11
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