Skin lesions are one of the typical symptoms of many diseases in humans and indicative of many types of cancer worldwide. Increased risks caused by the effects of climate change and a high cost of treatment, highlight the importance of skin cancer prevention efforts like this. The methods used to detect these diseases vary from a visual inspection performed by dermatologists to computational methods, and the latter has widely used automatic image classification applying Convolutional Neural Networks (CNNs) in medical image analysis in the last few years. This article presents an approach that uses CNNs with a NASNet architecture to recognize in a more accurate way, without segmentation, eight skin diseases. The model was trained end-to-end on Keras with augmented skin diseases images from the International Skin Imaging Collaboration (ISIC). The CNN architectures were initialized with weight from ImageNet, fine-tuned in order to discriminate well among the different types of skin lesions, and then 10-fold cross-validation was applied. Finally, some evaluation metrics are calculated as accuracy, sensitivity, and specificity and compare with other CNN trained architectures. This comparison shows that the proposed system offers higher accuracy results, with a significant reduction on the training paraments. To the best of our knowledge and based in the state-of-art recompiling in this work, the application of the NASNet architecture training with skin image lesion from ISIC archive for multi-class classification and evaluated by cross-validation, represents a novel skin disease classification system.

中文翻译：

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应用 NASNet 使用微调和数据增强进行多皮肤病变分类


皮肤病变是人类许多疾病的典型症状之一，也是全世界许多类型癌症的征兆。气候变化的影响和高昂的治疗费用造成的风险增加，凸显了此类皮肤癌预防工作的重要性。用于检测这些疾病的方法多种多样，从皮肤科医生进行的目视检查到计算方法，后者在过去几年中在医学图像分析中广泛使用应用卷积神经网络（CNN）的自动图像分类。本文提出了一种使用具有 NASNet 架构的 CNN 来更准确地识别八种皮肤病（无需分割）的方法。该模型在 Keras 上使用国际皮肤成像合作组织 (ISIC) 的增强皮肤病图像进行了端到端训练。 CNN 架构使用 ImageNet 的权重进行初始化，进行微调以更好地区分不同类型的皮肤病变，然后应用 10 倍交叉验证。最后，计算一些评估指标，如准确性、灵敏度和特异性，并与其他 CNN 训练的架构进行比较。这一比较表明，所提出的系统提供了更高的准确度结果，同时显着减少了训练参数。据我们所知，并基于本工作中最先进的重新编译，应用 NASNet 架构训练来自 ISIC 档案的皮肤图像病变进行多类分类并通过交叉验证进行评估，代表了一种新颖的皮肤疾病分类系统。