  COVID-19 is a global health problem. Consequently, early detection and
analysis of the infection patterns are crucial for controlling infection spread
as well as devising a treatment plan. This work proposes a two-stage deep
Convolutional Neural Networks (CNNs) based framework for delineation of
COVID-19 infected regions in Lung CT images. In the first stage, initially,
COVID-19 specific CT image features are enhanced using a two-level discrete
wavelet transformation. These enhanced CT images are then classified using the
proposed custom-made deep CoV-CTNet. In the second stage, the CT images
classified as infectious images are provided to the segmentation models for the
identification and analysis of COVID-19 infectious regions. In this regard, we
propose a novel semantic segmentation model CoV-RASeg, which systematically
uses average and max pooling operations in the encoder and decoder blocks. This
systematic utilization of max and average pooling operations helps the proposed
CoV-RASeg in simultaneously learning both the boundaries and region
homogeneity. Moreover, the idea of attention is incorporated to deal with
mildly infected regions. The proposed two-stage framework is evaluated on a
standard Lung CT image dataset, and its performance is compared with the
existing deep CNN models. The performance of the proposed CoV-CTNet is
evaluated using Mathew Correlation Coefficient (MCC) measure (0.98) and that of
proposed CoV-RASeg using Dice Similarity (DS) score (0.95). The promising
results on an unseen test set suggest that the proposed framework has the
potential to help the radiologists in the identification and analysis of
COVID-19 infected regions.
