融合三模型特征提升新冠肺部CT识别准确率
A Hybrid Deep Learning CNN Model for Enhanced COVID-19 Detection from Computed Tomography (CT) Scan Images
- 用VGG16、DenseNet121、MobileNetV2提取多维特征
- 在2481张肺部CT图像上达到98.93%准确率
- 适合临床辅助诊断，减轻医生阅片负担
早期发现新冠对治疗和防控至关重要。本研究提出一种新型混合深度学习模型，用于从肺部CT影像中检测新冠感染，旨在协助超负荷的医疗人员。该模型结合VGG16、DenseNet121和MobileNetV2三种预训练CNN进行特征提取，随后通过主成分分析（PCA）降维，再将特征拼接并由支持向量分类器（SVC）完成分类。我们对比了该混合模型与各独立模型的表现，使用包含2,108张训练图像和373张测试图像的数据集，涵盖新冠阳性与非新冠病例。结果表明，该混合模型达到98.93%的准确率，在精确率、召回率、F1分数及ROC曲线下面积方面均优于单个模型。
原文摘要 · Abstract (English)
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.
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