论文标题
COVID-19使用转移学习方法从计算机断层扫描图像检测
COVID-19 Detection Using Transfer Learning Approach from Computed Tomography Images
论文作者
论文摘要
在Covid-19大流行所带来的独特挑战中，有效，准确诊断的意义强调了创新方法的紧迫性。为了应对这些挑战，我们使用最近注释的计算机断层扫描（CT）图像数据库提出了一种基于转移学习的方法。尽管许多方法提出了密集的数据预启动和/或复杂的模型体系结构，但我们的方法着重于用最少的手动工程提供有效的解决方案。具体而言，我们研究了修改后的Xception模型对COVID-19检测的适用性。该方法涉及改编预先训练的X受感受模型，并结合来自Imagenet的体系结构和预训练的权重。该模型的输出旨在采取最终诊断决策。该培训利用了128个批次大小和224x224输入图像尺寸，从标准的512x512缩小。未在输入数据上进行进一步的DA处理。评估是在“ COV19-CT-DB” CT图像数据集上进行的，该数据集包含标记为COVID-19和非旋转19例。结果揭示了该方法在验证子集上的准确性，精度，回忆和宏F1分数，优于VGG-16传输模型，因此提供了更少的参数，因此提供了增强的精度。此外，与COV19-CT-DB数据集的替代方法相比，我们的方法超过了同一数据集上的基线方法和其他替代方法。最后，基于修改的Xception Trasnfer学习模型对COV19-CT-DB数据集的独特特征的适应性展示了其作为从CT图像增强COVID-19诊断的强大工具的潜力。
The significance of efficient and accurate diagnosis amidst the unique challenges posed by the COVID-19 pandemic underscores the urgency for innovative approaches. In response to these challenges, we propose a transfer learning-based approach using a recently annotated Computed Tomography (CT) image database. While many approaches propose an intensive data preproseccing and/or complex model architecture, our method focusses on offering an efficient solution with minimal manual engineering. Specifically, we investigate the suitability of a modified Xception model for COVID-19 detection. The method involves adapting a pre-trained Xception model, incorporating both the architecture and pre-trained weights from ImageNet. The output of the model was designed to take the final diagnosis decisions. The training utilized 128 batch sizes and 224x224 input image dimensions, downsized from standard 512x512. No further da processing was performed on the input data. Evaluation is conducted on the 'COV19-CT-DB' CT image dataset, containing labeled COVID-19 and non-COVID-19 cases. Results reveal the method's superiority in accuracy, precision, recall, and macro F1 score on the validation subset, outperforming VGG-16 transfer model and thus offering enhanced precision with fewer parameters. Furthermore, when compared to alternative methods for the COV19-CT-DB dataset, our approach exceeds the baseline approach and other alternatives on the same dataset. Finally, the adaptability of the modified Xception trasnfer learning-based model to the unique features of the COV19-CT-DB dataset showcases its potential as a robust tool for enhanced COVID-19 diagnosis from CT images.
