AUTCD-Net: An Automated Framework for Efficient Covid-19 Diagnosis on Computed Tomography Scans

ABSTRACT

The coronavirus pandemic has caused one of the biggest global crises. With an inevitable need for fast screening of the disease, deep learning-based segmentation of Covid-19 infected lung regions in computed tomography (CT) scans gained significant attention. The automated screening procedure generated results significantly faster than the manual screening techniques and directly helped provide a wider outreach to patients. Therefore, to aid in computer-aided diagnoses, this paper presents AUTCD-Net (AUTomated framework for efficient Covid-19 Diagnosis-Network), based on hierarchical resolution steps, to efficiently segment Covid-19 infected lung regions in CT scans. The approach results in a 0.71 dice score and rivals all previous state-of-the-art approaches. The overall evaluation combined with our in-depth model analysis, and critical inferences can be further extended for developing a computer-aided diagnostic (CAD) tool to assist the CT image reading process for detecting Covid-19 infected regions in the near future. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.