  Computed tomography (CT) imaging could be very practical for diagnosing
various diseases. However, the nature of the CT images is even more diverse
since the resolution and number of the slices of a CT scan are determined by
the machine and its settings. Conventional deep learning models are hard to
tickle such diverse data since the essential requirement of the deep neural
network is the consistent shape of the input data. In this paper, we propose a
novel, effective, two-step-wise approach to tickle this issue for COVID-19
symptom classification thoroughly. First, the semantic feature embedding of
each slice for a CT scan is extracted by conventional backbone networks. Then,
we proposed a long short-term memory (LSTM) and Transformer-based sub-network
to deal with temporal feature learning, leading to spatiotemporal feature
representation learning. In this fashion, the proposed two-step LSTM model
could prevent overfitting, as well as increase performance. Comprehensive
experiments reveal that the proposed two-step method not only shows excellent
performance but also could be compensated for each other. More specifically,
the two-step LSTM model has a lower false-negative rate, while the 2-step Swin
model has a lower false-positive rate. In summary, it is suggested that the
model ensemble could be adopted for more stable and promising performance in
real-world applications.
