Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning
Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning
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DOI：
10.3390/sym12040651
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发表时间：
2020-04-01
期刊：
影响因子：
2.7
通讯作者：
Khalifa, Nour Eldeen M.
中科院分区：
文献类型：
--
作者：
Loey, Mohamed;Smarandache, Florentin;Khalifa, Nour Eldeen M.
根据世界卫生组织(WHO)的说法，新冠肺炎(CoronaVirus)的大流行正在给世界各地的医疗系统带来前所未有的越来越大的压力。随着计算机算法的进步，特别是人工智能的进步，在早期阶段检测到这种病毒将有助于快速恢复，并有助于缓解医疗系统的压力。本文提出了一种用于胸部X光图像中冠状病毒检测的深度迁移学习GAN算法。缺乏新冠肺炎的数据集，特别是胸部X光图像，是这项科学研究的主要动机。其主要思想是收集到撰写本研究报告之前存在的所有可能的新冠肺炎图像，并使用GAN网络生成更多图像，以帮助从可用的X射线图像中以尽可能高的精度检测这种病毒。本研究中使用的数据集来自不同的来源，研究人员可以下载和使用它。收集的数据集中的图像数量是针对四种不同类型的类的307个图像。这些课程是新冠肺炎，正常，肺炎细菌和肺炎病毒。本研究选取了三种深度迁移模型进行研究。这些模型是Alexnet、GoogLeNet和Restnet18。通过本研究选择这些模型进行研究，因为它们的体系结构上只包含少量的层，这将降低所提出的模型的复杂性、内存消耗和执行时间。本文测试了三个案例场景，第一个场景包括数据集中的四个类，第二个场景包括三个类，第三个场景包括两个类。所有场景都包括新冠肺炎类，因为它是本研究要检测的主要对象。在第一个场景中，选择GoogLeNet作为主要的深度迁移模型，其测试准确率达到80.6%。在第二个场景中，选择Alexnet作为主要的深度迁移模型，因为它的测试准确率达到了85.2%；在第三个场景中，包括新冠肺炎和Normal两个类别，选择了GoogleNet作为主要的深度迁移模型，因为它达到了100%的测试准确率和99.9%的验证准确率。所有的绩效测量都通过研究加强了所获得的结果。
The coronavirus (COVID-19) pandemic is putting healthcare systems across the world under unprecedented and increasing pressure according to the World Health Organization (WHO). With the advances in computer algorithms and especially Artificial Intelligence, the detection of this type of virus in the early stages will help in fast recovery and help in releasing the pressure off healthcare systems. In this paper, a GAN with deep transfer learning for coronavirus detection in chest X-ray images is presented. The lack of datasets for COVID-19 especially in chest X-rays images is the main motivation of this scientific study. The main idea is to collect all the possible images for COVID-19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of this virus from the available X-rays images with the highest accuracy possible. The dataset used in this research was collected from different sources and it is available for researchers to download and use it. The number of images in the collected dataset is 307 images for four different types of classes. The classes are the COVID-19, normal, pneumonia bacterial, and pneumonia virus. Three deep transfer models are selected in this research for investigation. The models are the Alexnet, Googlenet, and Restnet18. Those models are selected for investigation through this research as it contains a small number of layers on their architectures, this will result in reducing the complexity, the consumed memory and the execution time for the proposed model. Three case scenarios are tested through the paper, the first scenario includes four classes from the dataset, while the second scenario includes 3 classes and the third scenario includes two classes. All the scenarios include the COVID-19 class as it is the main target of this research to be detected. In the first scenario, the Googlenet is selected to be the main deep transfer model as it achieves 80.6% in testing accuracy. In the second scenario, the Alexnet is selected to be the main deep transfer model as it achieves 85.2% in testing accuracy, while in the third scenario which includes two classes (COVID-19, and normal), Googlenet is selected to be the main deep transfer model as it achieves 100% in testing accuracy and 99.9% in the validation accuracy. All the performance measurement strengthens the obtained results through the research.