COVID-19 (Coronavirus) went through a rapid escalation until it became a pandemic disease. The normal and manual medical infection discovery may take few days and therefore computer science engineers can share in the development of the automatic diagnosis for fast detection of that disease. The study suggests a hybrid COVID-19 framework (named HMB-HCF) based on deep learning (DL), genetic algorithm (GA), weighted sum (WS), and majority voting principles in nine phases. Its segmentation phase suggests a lung segmentation algorithm using X-Ray images (named HMB-LSAXI) for extracting lungs. Its classification phase is built from a hybrid convolutional neural network (CNN) architecture using an abstractly-designed CNN (named HMB1-COVID19) and transfer learning (TL) pre-trained models (VGG16, VGG19, ResNet50, ResNet101, Xception, DenseNet121, DenseNet169, MobileNet, and MobileNetV2). The hybrid CNN architecture is used for learning, classification, and parameters optimization while GA is used to optimize the hyperparameters. This hybrid working mechanism is combined in an overall algorithm named HMB-DLGA. The study experiments implemented the WS approach to evaluate the models' performance using the loss, accuracy, F1-score, precision, recall, and area under curve (AUC) metrics with different pre-defined ratios. A collected, combined, and unified X-Ray dataset from 8 different public datasets was used alongside the regularization, dropout, and data augmentation techniques to limit the overall overfitting. The applied experiments reported state-of-the-art metrics. VGG16 reported 100% WS metric (i.e., 0.0097, 99.78%, 0.9984, 99.89%, 99.78%, and 0.9996 for the loss, accuracy, F1, precision, recall, and AUC respectively) concerning the highest WS. It also reported a 99.92% WS metric (i.e., 0.0099, 99.84%, 0.9984, 99.84%, 99.84%, and 0.9996 for the loss, accuracy, F1, precision, recall, and AUC respectively) concerning the last reported WS result. HMB-HCF was validated on 13 different public datasets to verify its generalization. The best-achieved metrics were compared with 13 related studies. These extensive experiments' target was the applicability verification and generalization.

COVID-19（冠状病毒）经历了快速升级，直至成为一种大流行病。正常和手动的医学感染发现可能需要几天时间，因此计算机科学工程师可以参与自动诊断的开发，以快速检测该疾病。该研究提出了一种基于深度学习 (DL)、遗传算法 (GA)、加权和 (WS) 和九个阶段的多数表决原则的混合 COVID-19 框架（名为 HMB-HCF）。它的分割阶段建议使用 X 射线图像（名为 HMB-LSAXI）提取肺部的肺部分割算法。其分类阶段基于混合卷积神经网络 (CNN) 架构，使用抽象设计的 CNN（名为 HMB1-COVID19）和迁移学习 (TL) 预训练模型（VGG16、VGG19、ResNet50、ResNet101、Xception、DenseNet121、DenseNet169、MobileNet 和 MobileNetV2）。混合 CNN 架构用于学习、分类和参数优化，而 GA 用于优化超参数。这种混合工作机制结合在一个名为 HMB-DLGA 的整体算法中。研究实验实施了 WS 方法，使用具有不同预定义比率的损失、准确度、F1 分数、精确度、召回率和曲线下面积 (AUC) 指标来评估模型的性能。来自 8 个不同公共数据集的收集、组合和统一的 X 射线数据集与正则化、丢弃和数据增强技术一起使用，以限制整体过度拟合。应用实验报告了最先进的指标。VGG16 报告了 100% WS 指标（即损失、准确度、F1、0.0097、99.78%、0.9984、99.89%、99.78% 和 0.9996，精度、召回率和 AUC 分别）关于最高 WS。它还报告了关于最后报告的 WS 结果的 99.92% WS 指标（即损失、准确性、F1、精度、召回率和 AUC 分别为 0.0099、99.84%、0.9984、99.84%、99.84% 和 0.9996）。HMB-HCF 在 13 个不同的公共数据集上进行了验证，以验证其泛化性。将最佳实现的指标与 13 项相关研究进行了比较。这些广泛的实验的目标是适用性验证和推广。HMB-HCF 在 13 个不同的公共数据集上进行了验证，以验证其泛化性。将最佳实现的指标与 13 项相关研究进行了比较。这些广泛的实验的目标是适用性验证和推广。HMB-HCF 在 13 个不同的公共数据集上进行了验证，以验证其泛化性。将最佳实现的指标与 13 项相关研究进行了比较。这些广泛的实验的目标是适用性验证和推广。