Two research papers submitted to arXiv propose novel methods for classifying COVID-19 cases from chest CT scans. The first paper introduces a hybrid 2D/3D Convolutional Neural Network (CNN) that extracts features from multiple planes within CT volumes, achieving 83.3% accuracy on a dataset of 1288 scans. The second paper presents a hybrid model combining a 3D CNN with a 3D MLP-Mixer, which leverages Vision Transformer-like architecture to capture both local and global image features, reaching 79.5% accuracy on a dataset of 1205 scans. Both methods aim to assist in the rapid diagnosis of COVID-19 and alleviate manpower shortages in medical institutions. AI
IMPACT These AI models could accelerate COVID-19 diagnosis and reduce strain on medical resources.
RANK_REASON Two academic papers published on arXiv detailing new AI methods for medical image analysis.
- 3D-convolutional neural network
- 3D MLP-Mixer
- arXiv
- CNN
- computed tomography
- COVID-19
- Masahiro Oda
- MLP-Mixer
- vision transformer
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