With the exponential growth of COVID-19 cases, medical practitioners are searching for accurate and quick automated detection methods to prevent Covid from spreading while trying to reduce the computational requirement of devices. In this research article, a deep learning Convolutional Neural Network (CNN) based accurate and efficient ensemble model using deep learning is being proposed with 2161 COVID-19, 2022 pneumonia, and 5863 normal chest X-ray images that has been collected from previous publications and other online resources. To improve the detection accuracy contrast enhancement and image normalization have been done to produce better quality images at the pre-processing level. Further data augmentation methods are used by creating modified versions of images in the dataset to train the four efficient CNN models (Inceptionv3, DenseNet121, Xception, InceptionResNetv2) Experimental results provide 98.33% accuracy for binary class and 92.36% for multiclass. The performance evaluation metrics reveal that this tool can be very helpful for early disease diagnosis.

中文翻译：

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一种使用 X 射线成像模式检测 COVID-19 的新型高效深度学习方法

随着 COVID-19 病例呈指数级增长，医疗从业人员正在寻找准确、快速的自动化检测方法，以防止 Covid 传播，同时试图降低设备的计算需求。在这篇研究文章中，提出了一种使用深度学习的基于深度学习卷积神经网络 (CNN) 的准确高效的集成模型，其中包含从以前的出版物中收集的 2161 幅 COVID-19、2022 年肺炎和 5863 幅正常胸部 X 射线图像和其他在线资源。为了提高检测精度，已经进行了对比度增强和图像归一化，以在预处理级别产生更好质量的图像。通过在数据集中创建图像的修改版本来使用进一步的数据增强方法来训练四个高效的 CNN 模型（Inceptionv3、DenseNet121、Xception, InceptionResNetv2) 实验结果为二元类提供了 98.33% 的准确率，为多类提供了 92.36% 的准确率。绩效评估指标表明，该工具对早期疾病诊断非常有帮助。