COVID-19 detection from pulmonary CT scans using a novel EfficientNet with attention mechanism
<|reference_start|>COVID-19 detection from pulmonary CT scans using a novel EfficientNet with attention mechanism: Manual analysis and diagnosis of COVID-19 through the examination of Computed Tomography (CT) images of the lungs can be time-consuming and result in errors, especially given high volume of patients and numerous images per patient. So, we address the need for automation of this task by developing a new deep learning model-based pipeline. Our motivation was sparked by the CVPR Workshop on "Domain Adaptation, Explainability and Fairness in AI for Medical Image Analysis", more specifically, the "COVID-19 Diagnosis Competition (DEF-AI-MIA COV19D)" under the same Workshop. This challenge provides an opportunity to assess our proposed pipeline for COVID-19 detection from CT scan images. The same pipeline incorporates the original EfficientNet, but with an added Attention Mechanism: EfficientNet-AM. Also, unlike the traditional/past pipelines, which relied on a pre-processing step, our pipeline takes the raw selected input images without any such step, except for an image-selection step to simply reduce the number of CT images required for training and/or testing. Moreover, our pipeline is computationally efficient, as, for example, it does not incorporate a decoder for segmenting the lungs. It also does not combine different backbones nor combine RNN with a backbone, as other pipelines in the past did. Nevertheless, our pipeline still outperforms all approaches presented by other teams in last year's instance of the same challenge, at least based on the validation subset of the competition dataset.<|reference_end|>
@article{farag2024covid-19,
    title={COVID-19 detection from pulmonary CT scans using a novel EfficientNet
  with attention mechanism},
    author={Ramy Farag, Parth Upadhyay, Yixiang Gao, Jacket Demby, Katherin Garces
  Montoya, Seyed Mohamad Ali Tousi, Gbenga Omotara, Guilherme DeSouza},
    journal={arXiv preprint arXiv:2403.11505},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11505},
    primaryClass={eess.IV cs.CV cs.LG}
}