基于深度卷积神经网络的色素性皮肤病识别分类

针对当前皮肤病识别分类面临的两个主要问题：一是由于皮肤病种类繁多，病灶外观的类间相似度高和类内差异化大，尤其是色素性皮肤病，使得皮肤病的识别分类比较困难；二是皮肤病识别算法模型设计存在一定的局限性，识别率还有待进一步提高。为此，以VGG19模型为基础架构，训练了一个结构化的深度卷积神经网络（CNN），实现了色素性皮肤病的自动分类。首先，采用数据增强（裁剪、翻转、镜像）对数据进行预处理；其次，将在ImageNet上预训练好的模型，迁移至增强后的数据集进行调优训练，训练过程中通过设置Softmax损失函数的权重，增加少数类判别错误的损失，来缓解数据集中存在的类别不平衡问题，提高模型的识别率。实验采用深度学习框架PyTorch，在数据集ISIC2017上进行。实验结果表明，该方法的识别率和敏感性可分别达到71.34%、70.01%，相比未设置损失函数的权重时分别提高了2.84、11.68个百分点，说明该方法是一种有效的皮肤病识别分类方法。

Pigmented skin lesion recognition and classification based on deep convolutional neural network

Currently, the recognition and classification of skin lesions faces two major challenges. First, the wide variety of skin lesions, the high similarity between different classes, and the large differences within the same class, especially pigmented skin lesions, make it difficult to identify and classify skin lesions. Second, as the limitations of the recognition algorithms of skin lesions, the recognition rates of the algorithms need to be further improved. To this end, an end-to-end structured deep Convolutional Neural Network (CNN) model was trained based on VGG19 network to achieve automated recognition and classification of pigmented skin lesions. Firstly, a data augmentation method (random crop, flip, mirror) was used for data preprocessing. Then, the pre-trained model from ImageNet was transferred to the augmented data samples to fine-tune the parameters. Meanwhile, by setting a weight of Softmax loss, the loss of minority class discriminant errors was increased to effectively alleviate the class-imbalance problem in the dataset. As a result, the recognition rate of the model was improved. Experiments were implemented on the dataset ISIC2017 using the deep learning framework PyTorch. The experimental results show that the recognition rate and sensitivity of the proposed method can reach 71.34% and 70.01%, respectively, which are 2.84 and 11.68 percentage points higher than those without the weight of Softmax loss. It is confirmed that our method is effective in the recognition and classification of skin lesions.