CovTANet: A Hybrid Tri-Level Attention-Based Network for Lesion Segmentation, Diagnosis, and Severity Prediction of COVID-19 Chest CT Scans

09, September 2021 |

Authors:

Abstract

Rapid and precise diagnosis of COVID-19 is one of the major challenges faced by the global community to control the spread of this overgrowing pandemic. In this article, a hybrid neural network is proposed, named Cov- TANet, to provide an end-to-end clinical diagnostic tool for early diagnosis, lesion segmentation, and severity predic- tion of COVID-19 utilizing chest computer tomography (CT) scans. A multiphase optimization strategy is introduced for solving the challenges of complicated diagnosis at a very early stage of infection, where an efficient lesion segmenta- tion network is optimized initially, which is later integrated into a joint optimization framework for the diagnosis and severity prediction tasks providing feature enhancement of the infected regions. Moreover, for overcoming the chal- lenges with diffused, blurred, and varying shaped edges of COVID lesions with novel and diverse characteristics, a novel segmentation network is introduced, namely tri- level attention-based segmentation network. This network has significantly reduced semantic gaps in subsequent encoding–decoding stages, with immense parallelization of multiscale features for faster convergence providing con- siderable performance improvement over traditional net- works. Furthermore, a novel tri-level attention mechanism has been introduced, which is repeatedly utilized over the network, combining channel, spatial, and pixel attention schemes for faster and efficient generalization of contex- tual information embedded in the feature map through fea- ture recalibration and enhancement operations. Outstand- ing performances have been achieved in all three tasks through extensive experimentation on a large publicly avail- able dataset containing 1110 chest CT-volumes, which sig- nifies the effectiveness of the proposed scheme at the cur- rent stage of the pandemic.