COVID-19 Detection using Deep Learning

V. Gupta
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引用次数: 0

Abstract

Deep learning is an artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning and recognizing patterns from data that is unstructured or unlabelled. It is also known as deep neural learning or deep neural network. Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets have been successful in identifying faces, objects and traffic signs apart from powering vision in robots and self-driving cars. ConvNets can also be used for Radio Imaging which helps in disease detection. This paper helps in detecting COVID-19 from the X-ray images provided to the model using Convolutional Neural Networks (CNN) and image augmentation techniques.
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利用深度学习检测COVID-19
深度学习是一种人工智能功能,它模仿人脑在处理数据和创建用于决策的模式方面的工作方式。深度学习是人工智能(AI)中机器学习的一个子集,它具有能够从非结构化或未标记的数据中学习和识别模式的网络。它也被称为深度神经学习或深度神经网络。卷积神经网络(ConvNets或cnn)是神经网络的一个类别,已被证明在图像识别和分类等领域非常有效。除了为机器人和自动驾驶汽车提供视觉支持外,卷积神经网络还在识别人脸、物体和交通标志方面取得了成功。卷积神经网络也可以用于无线电成像,这有助于疾病检测。本文利用卷积神经网络(CNN)和图像增强技术,帮助从提供给模型的x射线图像中检测COVID-19。
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