This paper investigates the application of deep learning models for lung Computed Tomography (CT) image analysis. Traditional deep learning frameworks encounter compatibility issues due to variations in slice numbers and resolutions in CT images, which stem from the use of different machines. Commonly, individual slices are predicted and subsequently merged to obtain the final result; however, this approach lacks slice-wise feature learning and consequently results in decreased performance. We propose a novel slice selection method for each CT dataset to address this limitation, effectively filtering out uncertain slices and enhancing the model's performance. Furthermore, we introduce a spatial-slice feature learning (SSFL) technique\cite{hsu2022} that employs a conventional and efficient backbone model for slice feature training, followed by extracting one-dimensional data from the trained model for COVID and non-COVID classification using a dedicated classification model. Leveraging these experimental steps, we integrate one-dimensional features with multiple slices for channel merging and employ a 2D convolutional neural network (CNN) model for classification. In addition to the aforementioned methods, we explore various high-performance classification models, ultimately achieving promising results.

翻译：本文研究了深度学习模型在肺部计算机断层扫描（CT）图像分析中的应用。由于不同设备导致CT图像的切片数量和分辨率存在差异，传统深度学习框架面临兼容性问题。常见方法是对单个切片进行预测，随后合并结果，但这种方法缺乏切片级特征学习，导致性能下降。为克服这一局限，我们提出了一种针对各CT数据集的切片筛选方法，有效过滤不确定切片并提升模型性能。此外，我们引入空间-切片特征学习技术（SSFL）\cite{hsu2022}，该方法利用常规高效的主干网络进行切片特征训练，随后从训练后的模型中提取一维特征，通过专用分类模型进行COVID与非COVID分类。基于上述实验步骤，我们将一维特征与多切片信息进行通道合并，采用二维卷积神经网络（CNN）模型进行分类。除上述方法外，我们还探索了多种高性能分类模型，最终取得了显著成果。