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

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 paper, a hybrid neural network is proposed, named CovTANet, to provide an end-to-end clinical diagnostic tool for early diagnosis, lesion segmentation, and severity prediction of COVID-19 utilizing chest computer tomography (CT) scans. A multi-phase optimization strategy is introduced for solving the challenges of complicated diagnosis at a very early stage of infection, where an efficient lesion segmentation 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 challenges 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 (TA-SegNet). This network has significantly reduced semantic gaps in subsequent encoding decoding stages, with immense parallelization of multi-scale features for faster convergence providing considerable performance improvement over traditional networks. 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 contextual information embedded in the feature map through feature recalibration and enhancement operations. Outstanding performances have been achieved in all three-tasks through extensive experimentation on a large publicly available dataset containing 1110 chest CT-volumes that signifies the effectiveness of the proposed scheme at the current stage of the pandemic.

A comparative study of X-ray and CT images in COVID-19 detection using image processing and deep learning techniques

“…An accuracy of 99.1% is obtained with low computational complexity. Tanvir Mahmud et al [73] proposed a hybrid neural network for COVID-19 early diagnosis and severity prediction from CT scans named CovTANet. The network obtained an accuracy of 95.8% for severity prediction which used a segmentation network called TA-SegNet for lesion segmentation.…”

SCOAT-Net: A novel network for segmenting COVID-19 lung opacification from CT images

“…Another recent model proposed in Mahmud et al. [41] not only includes spatial- and channel-level attentions but also introduces pixel-level attention to supplement the low-level features, which adds more model parameters. In contrast, to realize the integration of context features of various levels, our SCOAT-Net introduces skip connections to integrate the features of lower level with that of current level, without introducing additional parameters.…”

COVID-19 Infection Segmentation from Chest CT Images Based on Scale Uncertainty

“…Automatic segmentation of them is an essential component of CAD systems. Related Work of COVID-19 Segmentation: Previously, deep learningbased automatic segmentation methods of infection regions from CT volumes of COVID-19 cases were proposed [4,5,6,7,8]. Fan et al [4] proposed an infection region segmentation method using the Inf-Net.…”

“…However, because they employ 2D image-based process, 3D positional information is not utilized in their segmentation method. Other papers also employ 2D image-based process [5,6,7]. Yan et al [8] proposed a fully convolutional network (FCN) to segment infection and normal regions in the lung.…”