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首页> 外文期刊>Informatics in Medicine Unlocked >Skin cancer detection: Applying a deep learning based model driven architecture in the cloud for classifying dermal cell images
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Skin cancer detection: Applying a deep learning based model driven architecture in the cloud for classifying dermal cell images

机译：皮肤癌检测：在云中应用基于深入的学习模型驱动架构进行分类真皮细胞图像

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Background: Skin cancer is a common form of cancer, and early detection increases the survival rate. Objective: To build deep learning models to classify dermal cell images and detect skin cancer. Methods: A model-driven architecture in the cloud, that uses deep learning algorithms in its core implementations, is used to construct models that assist in predicting skin cancer with improved accuracy. The study illustrates the method of building models and applying them to classify dermal cell images. Results: The deep learning models built here are tested on standard datasets, and the metric area under the curve of 99.77% was observed. Conclusions: A practitioner can use the model-driven architecture and quickly build the deep learning models to predict skin cancer.
机译：背景：皮肤癌是一种常见的癌症形式，早期检测增加了存活率。目的：建设深层学习模型，分类真皮细胞图像并检测皮肤癌。方法：在云中使用深度学习算法的云中的模型驱动架构用于构建有助于预测皮肤癌的模型，以提高精度。该研究说明了构建模型的方法并将它们应用于分类真皮细胞图像。结果：在标准数据集中建立的深层学习模型，观察到99.77％的曲线下的公制区域。结论：从业者可以使用模型驱动的架构并快速构建深度学习模型来预测皮肤癌。

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