April 2, 2024, 7:44 p.m. | Zhaoshan Liu, Lei Shen

cs.LG updates on arXiv.org arxiv.org

arXiv:2302.02314v4 Announce Type: replace-cross
Abstract: The COVID-19 pandemic has resulted in hundreds of million cases and numerous deaths worldwide. Here, we develop a novel classification network CECT by controllable ensemble convolutional neural network and transformer to provide a timely and accurate COVID-19 diagnosis. The CECT is composed of a parallel convolutional encoder block, an aggregate transposed-convolutional decoder block, and a windowed attention classification block. Each block captures features at different scales from 28 $\times$ 28 to 224 $\times$ 224 from …

arxiv classification cnn covid covid-19 cs.cv cs.lg eess.iv ensemble image transformer type

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