LwMLA-NET: A Lightweight Multi-Level Attention-Based NETwork for Segmentation of COVID-19 Lungs Abnormalities From CT Images

COronaVIrus Disease 2019 (COVID-19) emerged as a global pandemic in the last two years. Typical abnormal findings in chest computed tomography (CT) images of COVID-19 patients are ground-glass opacities (GGOs) and consolidation, which signify the extent of damage caused to the lungs. The manual annotation of these abnormalities for severity analysis is complex, tedious, and time-consuming. This motivated us to develop a vision-based analysis framework for automated segmentation of lung abnormalities. We proposed a deep learning framework, namely LwMLA-NET Lightweight Multi-Level Attention-based NETwork, to segment GGO and consolidation. The LwMLA-NET is based on an encoder-decoder architecture where depth-wise separable convolutions are employed at each stage, making it a light-weighted framework that significantly reduces the computational cost. Another distinguishable module in LwMLA-NET is the multilevel attention (MLA) mechanism that focuses on dominant and relevant features and avoids propagation of insignificant features from the encoder to the decoder, thereby aiding faster optimization. Integrating the atrous spatial pyramid pooling (ASPP) module in the bottleneck helps to handle scale variations. The LwMLA-NET was evaluated on two databases-MedSeg and Radiopedia-and obtained F1-scores of 76.7% and 73.1%, respectively. The experimental evaluation justifies that LwMLA-NET outperforms other state-of-the-art deep learning frameworks like Attention U-Net, PSP-Net, Cople-Net, Inf-Net, and Mobile-Net V2 in terms of segmentation performance and has an acceptable generalization capability. Moreover, our team, LwMLA-NET-Team-KDD-JU, participated in Kaggle's COVID-19 CT Images Segmentation open challenge. The performance was evaluated on a separate test set, and we obtained fourth rank on the leaderboard among 40 teams with an F1-score of 71.196%.

Automated summary

COVID-19, a global pandemic, has typical abnormal findings in chest CT images such as ground-glass opacities (GGOs) and consolidation. Manual annotation of these abnormalities is complex and time-consuming, so we developed a vision-based analysis framework for automated segmentation of lung abnormalities. Our deep learning framework, LwMLA-NET, outperforms other state-of-the-art deep learning frameworks in terms of segmentation performance and has acceptable generalization capability.