用深度学习模型辅助银屑病诊断，Inception v3表现最佳。
A Deep Learning Application for Psoriasis Detection
- 比较ResNet50、Inception v3和VGG19在银屑病图像分类中的表现。
- Inception v3准确率和F1分数达97.5% ± 0.2，最优。
- 适合皮肤科医生辅助诊断，尤其对影像识别需求高者。
本文对比了三种卷积神经网络模型——ResNet50、Inception v3和VGG19——在银屑病皮肤病变图像分类中的性能。训练与验证所用图像来自专业平台。通过调整评估指标，结果表明Inception v3在准确率和F1分数上表现优异，达97.5% ± 0.2，是辅助银屑病诊断的有力工具。
原文摘要 · Abstract (English)
In this paper a comparative study of the performance of three Convolutional Neural Network models, ResNet50, Inception v3 and VGG19 for classification of skin images with lesions affected by psoriasis is presented. The images used for training and validation of the models were obtained from specialized platforms. Some techniques were used to adjust the evaluation metrics of the neural networks. The results found suggest the model Inception v3 as a valuable tool for supporting the diagnosis of psoriasis. This is due to its satisfactory performance with respect to accuracy and F1-Score (97.5% ${\pm}$ 0.2).
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