Abstract
Vitiligo is one of the most intractable skin disease in the world. According to incomplete statistics, there is about 0.5–2% incidence of vitiligo in the world, and the number is still growing, so the early diagnosis of vitiligo is very important. In recent years, deep learning has been successfully applied to medical image classification and has achieved outstanding performance, which helps achieve vitiligo intelligent diagnosis. In this paper, we propose a method base on probability-average value of three convolutional neural network (CNN) models which are same structures, and trained with three different color-space images (RGB, HSV, and YCrCb) for the same vitiligo dataset. The applied strategy is found to achieve the classification performance of 94.2% area under the roc curve (AUC), 87.8% accuracy, 91.9% precision, 90.9% sensitivity, 80.2% specificity which outperforms the individual networks.
This work is supported by Intelligent Manufacturing Standardization Program of Ministry of Industry and Information Technology (No. 2016ZXFB01001).
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Liu, J., Yan, J., Chen, J., Sun, G., Luo, W. (2019). Classification of Vitiligo Based on Convolutional Neural Network. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11633. Springer, Cham. https://doi.org/10.1007/978-3-030-24265-7_19
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DOI: https://doi.org/10.1007/978-3-030-24265-7_19
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