  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.
