Since the emergence of Covid-19 in late 2019, medical image analysis using
artificial intelligence (AI) has emerged as a crucial research area,
particularly with the utility of CT-scan imaging for disease diagnosis. This
paper contributes to the 4th COV19D competition, focusing on Covid-19 Detection
and Covid-19 Domain Adaptation Challenges. Our approach centers on lung
segmentation and Covid-19 infection segmentation employing the recent CNN-based
segmentation architecture PDAtt-Unet, which simultaneously segments lung
regions and infections. Departing from traditional methods, we concatenate the
input slice (grayscale) with segmented lung and infection, generating three
input channels akin to color channels. Additionally, we employ three 3D CNN
backbones Customized Hybrid-DeCoVNet, along with pretrained 3D-Resnet-18 and
3D-Resnet-50 models to train Covid-19 recognition for both challenges.
Furthermore, we explore ensemble approaches and testing augmentation to enhance
performance. Comparison with baseline results underscores the substantial
efficiency of our approach, with a significant margin in terms of F1-score (14
%). This study advances the field by presenting a comprehensive methodology for
accurate Covid-19 detection and adaptation, leveraging cutting-edge AI
techniques in medical image analysis.

这篇论文通过使用人工智能技术进行医学图像分析，特别是利用 CT 扫描图像进行疾病诊断，探讨了准确的 Covid-19 检测和适应问题，并提出了基于 CNN 的分割方法来分割肺部区域和感染部位。研究结果表明，该方法在 F1-score 方面表现出高效率和显著改进。