标题： 基于 computed tomography (CT) 扫描图像的增强型 COVID-19 检测的混合深度学习 CNN 模型 显示英文标题

标题： A Hybrid Deep Learning CNN Model for Enhanced COVID-19 Detection from Computed Tomography (CT) Scan Images

摘要： 早期检测 COVID-19 对于有效治疗和控制其传播至关重要。 本研究提出了一种新颖的混合深度学习模型，用于从 CT 扫描图像中检测 COVID-19，旨在协助负担过重的医疗专业人员。 我们提出的模型利用了 VGG16、DenseNet121 和 MobileNetV2 的优势来提取特征，随后通过主成分分析（PCA）进行降维，然后将特征堆叠并使用支持向量分类器（SVC）进行分类。 我们对所提出的混合模型与各个预训练的 CNN 模型进行了比较分析，使用了一个包含 2,108 张训练图像和 373 张测试图像的数据集，其中包括 COVID 阳性和非 COVID 图像。 我们提出的混合模型达到了 98.93% 的准确率，在精确度、召回率、F1 分数和 ROC 曲线性能方面优于各个单独模型。 显示英文摘要

摘要： Early detection of COVID-19 is crucial for effective treatment and controlling its spread. This study proposes a novel hybrid deep learning model for detecting COVID-19 from CT scan images, designed to assist overburdened medical professionals. Our proposed model leverages the strengths of VGG16, DenseNet121, and MobileNetV2 to extract features, followed by Principal Component Analysis (PCA) for dimensionality reduction, after which the features are stacked and classified using a Support Vector Classifier (SVC). We conducted comparative analysis between the proposed hybrid model and individual pre-trained CNN models, using a dataset of 2,108 training images and 373 test images comprising both COVID-positive and non-COVID images. Our proposed hybrid model achieved an accuracy of 98.93%, outperforming the individual models in terms of precision, recall, F1 scores, and ROC curve performance.