Collective Intelligent Strategy for Improved Segmentation of COVID-19 from CT

TL;DR

This paper introduces EAMC, a deep learning ensemble model with attention mechanisms, for accurate, fast, and non-invasive COVID-19 lung infection segmentation from CT scans, demonstrating superior performance over existing models.

Contribution

The study presents a novel ensemble deep learning model with attention modules and LOPO training for improved COVID-19 segmentation, especially under class imbalance, outperforming state-of-the-art methods.

Findings

EAMC achieves higher sensitivity and precision than existing models.

The model performs well across four public COVID-19 datasets.

It effectively handles class imbalance in COVID-19 CT segmentation.

Abstract

The devastation caused by the coronavirus pandemic makes it imperative to design automated techniques for a fast and accurate detection. We propose a novel non-invasive tool, using deep learning and imaging, for delineating COVID-19 infection in lungs. The Ensembling Attention-based Multi-scaled Convolution network (EAMC), employing Leave-One-Patient-Out (LOPO) training, exhibits high sensitivity and precision in outlining infected regions along with assessment of severity. The Attention module combines contextual with local information, at multiple scales, for accurate segmentation. Ensemble learning integrates heterogeneity of decision through different base classifiers. The superiority of EAMC, even with severe class imbalance, is established through comparison with existing state-of-the-art learning models over four publicly-available COVID-19 datasets. The results are suggestive of the relevance of deep learning in providing assistive intelligence to medical practitioners, when they are overburdened with patients as in pandemics. Its clinical significance lies in its unprecedented scope in providing low-cost decision-making for patients lacking specialized healthcare at remote locations.