Simple 2D Convolutional Neural Network-based Approach for COVID-19
  Detection
Chih-Chung Hsu Chia-Ming Lee Yang Fan Chiang Yi-Shiuan Chou Chih-Yu Jiang Shen-Chieh Tai Chi-Han Tsai
This study explores the use of deep learning techniques for analyzing lung
Computed Tomography (CT) images. Classic deep learning approaches face
challenges with varying slice counts and resolutions in CT images, a diversity
arising from the utilization of assorted scanning equipment. Typically,
predictions are made on single slices which are then combined for a
comprehensive outcome. Yet, this method does not incorporate learning features
specific to each slice, leading to a compromise in effectiveness. To address
these challenges, we propose an advanced Spatial-Slice Feature Learning
(SSFL++) framework specifically tailored for CT scans. It aims to filter out
out-of-distribution (OOD) data within the entire CT scan, allowing us to select
essential spatial-slice features for analysis by reducing data redundancy by
70\%. Additionally, we introduce a Kernel-Density-based slice Sampling (KDS)
method to enhance stability during training and inference phases, thereby
accelerating convergence and enhancing overall performance. Remarkably, our
experiments reveal that our model achieves promising results with a simple
EfficientNet-2D (E2D) model. The effectiveness of our approach is confirmed on
the COVID-19-CT-DB datasets provided by the DEF-AI-MIA workshop.