Spatiotemporal Feature Learning Based on Two-Step LSTM and Transformer for CT Scans

TL;DR

This paper introduces a two-step LSTM and Transformer-based approach for analyzing CT scans to improve COVID-19 diagnosis by effectively handling diverse data and capturing spatiotemporal features.

Contribution

The study proposes a novel two-step framework combining semantic feature embedding with LSTM and Transformer models for enhanced CT scan analysis.

Findings

Two-step LSTM model reduces false negatives.

Ensemble of models improves stability and performance.

Proposed method outperforms conventional approaches.

Abstract

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.