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A depthwise separable dense convolutional network with convolution block attention module for COVID-19 diagnosis on

Qian Li1, Jiangbo Ning2, Jianping Yuan2

  • 1Research Center for Ultrasonics and Technologies, Institute of Acoustics, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China.

Computers in Biology and Medicine
|September 16, 2021
PubMed
Summary

Researchers developed a new lightweight AI model, AM-SdenseNet, for faster and more accurate COVID-19 diagnosis from CT scans. This model utilizes the largest public dataset of COVID-19 positive CT scans, COVID-CTx, to aid global health efforts.

Keywords:
COVID-19 diagnosisCT scanConvolution block attention moduleDataset light-weightedDenseNetDepthwise separable convolution

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Coronavirus disease 2019 (COVID-19) poses a significant global health threat, necessitating rapid diagnostic tools.
  • Existing deep learning models for COVID-19 detection from CT scans often lack publicly available datasets and are computationally intensive.
  • Privacy concerns and computational limitations hinder the development and widespread application of AI-driven diagnostic tools.

Purpose of the Study:

  • To address the limitations of existing COVID-19 diagnostic AI models by creating a publicly accessible dataset and a computationally efficient network.
  • To introduce the COVID-CTx dataset, the largest collection of publicly available COVID-19 positive CT scans.
  • To propose and evaluate a lightweight hybrid neural network, AM-SdenseNet, for improved COVID-19 diagnosis.

Main Methods:

  • Established the COVID-CTx dataset comprising 828 COVID-19 positive CT scans from 324 patients, sourced from open-access repositories.
  • Developed a lightweight hybrid neural network, AM-SdenseNet, integrating a Convolutional Block Attention Module (AM) with depthwise separable convolutions.
  • Evaluated the performance of AM-SdenseNet against state-of-the-art baseline models using the COVID-CTx dataset.

Main Results:

  • The proposed AM-SdenseNet demonstrated superior performance compared to several state-of-the-art baseline models in diagnosing COVID-19 from CT scans.
  • The lightweight architecture of AM-SdenseNet effectively reduces model parameters, mitigating overfitting issues.
  • The COVID-CTx dataset provides a valuable, large-scale resource for further research in AI-based COVID-19 detection.

Conclusions:

  • AM-SdenseNet offers a promising solution for rapid and accurate COVID-19 diagnosis, suitable for computationally limited platforms.
  • The availability of the COVID-CTx dataset facilitates broader research and development in AI-assisted medical imaging for infectious diseases.
  • The study highlights the potential of efficient deep learning models in enhancing global public health responses to pandemics.