A free web service for fast COVID-19 classification of chest X-Ray images
A free web service for fast COVID-19 classification of chest X-Ray images
The coronavirus outbreak became a major concern for society worldwide. Technological innovation and ingenuity are essential to fight COVID-19 pandemic and bring us one step closer to overcome it.Researchers over the world are working actively to find available alternatives in different fields, such as the Healthcare System, pharmaceutic, health prevention, among others. With the rise of artificial intelligence (AI) in the last 10 years, IA-based applications have become the prevalent solution in different areas because of its higher capability, being now adopted to help combat against COVID-19. This work provides a fast detection system of COVID-19 characteristics in X-Ray images based on deep learning (DL) techniques. This system is available as a free web deployed service for fast patient classification, alleviating the high demand for standards method for COVID-19 diagnosis. It is constituted of two deep learning models, one to differentiate between X-Ray and non-X-Ray images based on Mobile-Net architecture, and another one to identify chest X-Ray images with characteristics of COVID-19 based on the DenseNet architecture. For real-time inference, it is provided a pair of dedicated GPUs, which reduce the computational time. The whole system can filter out non-chest X-Ray images, and detect whether the X-Ray presents characteristics of COVID-19, highlighting the most sensitive regions.
免费的Web服务，可对胸部X射线图像进行快速COVID-19分类
冠状病毒的爆发已成为全世界社会关注的主要问题。技术创新和创造力对于对抗COVID-19大流行至关重要，并使我们更进一步地克服了这一大流行。.. 世界各地的研究人员正在积极寻找不同领域的可用替代方案，例如医疗保健系统，药物，健康预防等。在过去的十年中，随着人工智能（AI）的兴起，基于IA的应用程序由于具有更高的功能而已成为不同领域的普遍解决方案，现已被用来帮助对抗COVID-19。这项工作提供了基于深度学习（DL）技术的X射线图像中COVID-19特征的快速检测系统。该系统可作为免费的网络部署服务提供，用于快速患者分类，从而减轻了对COVID-19诊断标准方法的高要求。它由两种深度学习模型组成，一种基于Mobile-Net架构区分X射线图像和非X射线图像，另一个基于DenseNet架构识别具有COVID-19特征的胸部X射线图像。为了进行实时推理，它提供了一对专用GPU，可减少计算时间。整个系统可以过滤掉非胸部的X射线图像，并检测X射线是否表现出COVID-19的特征，从而突出显示最敏感的区域。 （阅读更多）
