Severity classification of ground-glass opacity via 2-D convolutional neural network and lung CT scans: a 3-day exploration. (arXiv:2303.16904v1 [eess.IV])

Ground-glass opacity is a hallmark of numerous lung diseases, including
patients with COVID19 and pneumonia. 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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