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Ground-glass opacity is a hallmark of numerous lung diseases, includingpatients with COVID19 and pneumonia, pulmonary fibrosis, and tuberculosis. Thisbrief note presents experimental results of a proof-of-concept framework thatgot implemented and tested over three days as driven by the third challengeentitled "COVID-19 Competition", hosted at the AI-Enabled Medical ImageAnalysis Workshop of the 2023 IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP 2023). Using a newly built virtualenvironment (created on March 17, 2023), we investigated various pre-trainedtwo-dimensional convolutional neural networks (CNN) such as Dense NeuralNetwork, Residual Neural Networks (ResNet), and Vision Transformers, as well asthe extent of fine-tuning. Based on empirical experiments, we opted tofine-tune them using ADAM's optimization algorithm with a standard learningrate of 0.001 for all CNN architectures and apply early-stopping whenever thevalidation loss reached a plateau. For each trained CNN, the model state withthe best validation accuracy achieved during training was stored and laterreloaded for new classifications of unseen samples drawn from the validationset provided by the challenge organizers. According to the organizers, few ofthese 2D CNNs yielded performance comparable to an architecture that combinedResNet and Recurrent Neural Network (Gated Recurrent Units). As part of thechallenge requirement, the source code produced during the course of thisexercise is posted at https://github.com/lisatwyw/cov19. We also hope thatother researchers may find this light prototype consisting of few Python filesbased on PyTorch 1.13.1 and TorchVision 0.14.1 approachable.

Severity classification of ground-glass opacity via 2-D convolutional neural network and lung CT scans: a 3-day exploration