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Automatic Detection of Skin Surface Structure Using Deep Learning for the Impression Mold Technique

机译:深度学习对印模模具技术的自动检测皮肤表面结构

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This paper presents a discriminator that automatically discriminates skin ridges and folds by performing image analysis using a discriminator that uses deep learning. We use U-Net as the discriminator and a data set including images of the thighs, forearm, chest, forehead, and other parts of healthy controls and atopic dermatitis. We evaluate the discriminator using the mDice coefficient, which is the average of the Dice coefficient of the skin ridges and the Dice coefficient of the skin folds of each part. As a result, for 96 images of a test data, the average mDice coefficients for healthy controls and atopic dermatitis are 0.92 and 0.96, respectively.
机译:本文呈现了一种鉴别器,通过使用使用深度学习的鉴别器来进行图像分析,自动判别皮肤脊和折叠。 我们使用U-Net作为鉴别器和数据集,包括大腿,前臂,胸部,额头和健康控制和特应性皮炎的其他部分的图像。 我们使用MDICE系数评估鉴别器,这是皮肤脊的骰子系数的平均值和每个部分的皮肤褶皱的骰子系数。 结果,对于测试数据的96个图像,健康对照和特应性皮炎的平均MDICE系数分别为0.92和0.96。

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