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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F22%3A50019099" target="_blank" >RIV/62690094:18450/22:50019099 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/9740186" target="_blank" >https://ieeexplore.ieee.org/document/9740186</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TIM.2022.3161690" target="_blank" >10.1109/TIM.2022.3161690</a>

Result language

angličtina

Original language name

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

Original language description

COVID-19 emerged as a global pandemic in past two years. Typical abnormal findings in chest CT images of COVID-19 patients are ground glass opacities (GGO) and consolidation, which signify the extent of damage caused to the lungs. The manual annotation of those abnormalities for severity analysis is complex, tedious, and time-consuming. This motivated us to develop a vision-based analysis framework for automated segmentation of lungs 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 which 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 4th rank on the leaderboard among 40 teams with an F1-score of 71.196%. IEEE

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20201 - Electrical and electronic engineering

Project

<a href="/en/project/EF18_069%2F0010054" target="_blank" >EF18_069/0010054: IT4Neuro(degeneration)</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE Transactions on Instrumentation and Measurement

ISSN

0018-9456

e-ISSN

1557-9662

Volume of the periodical

71

Issue of the periodical within the volume

March

Country of publishing house

US - UNITED STATES

Number of pages

13

Pages from-to

"Article Number: 5007813"

UT code for WoS article

000778909800005

EID of the result in the Scopus database

2-s2.0-85127065324