Severity classification of ground-glass opacity via 2-D convolutional neural network and lung CT scans: a 3-day exploration. (arXiv:2303.16904v2 [eess.IV])

Ground-glass opacity is a hallmark of numerous lung diseases, including
patients with COVID19 and pneumonia, pulmonary fibrosis, and tuberculosis. This
brief note presents experimental results of a proof-of-concept framework that
got implemented and tested over three days as driven by the third challenge
entitled “COVID-19 Competition”, hosted at the AI-Enabled Medical Image
Analysis Workshop of the 2023 IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2023). Using a newly built virtual
environment (created on March 17, 2023), we investigated various pre-trained
two-dimensional convolutional neural networks (CNN) such as Dense Neural
Network, Residual Neural Networks (ResNet), and Vision Transformers, as well as
the extent of fine-tuning. Based on empirical experiments, we opted to
fine-tune them using ADAM’s optimization algorithm with a standard learning
rate of 0.001 for all CNN architectures and apply early-stopping whenever the
validation loss reached a plateau. For each trained CNN, the model state with
the best validation accuracy achieved during training was stored and later
reloaded for new classifications of unseen samples drawn from the validation
set provided by the challenge organizers. According to the organizers, few of
these 2D CNNs yielded performance comparable to an architecture that combined
ResNet and Recurrent Neural Network (Gated Recurrent Units). As part of the
challenge requirement, the source code produced during the course of this
exercise is posted at We also hope that
other researchers may find this light prototype consisting of few Python files
based on PyTorch 1.13.1 and TorchVision 0.14.1 approachable.



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