  COVID-19 is a new pathogen that first appeared in the human population at the
end of 2019, and it can lead to novel variants of pneumonia after infection.
COVID-19 is a rapidly spreading infectious disease that infects humans faster.
Therefore, efficient diagnostic systems may accurately identify infected
patients and thus help control their spread. In this regard, a new two-stage
analysis framework is developed to analyze minute irregularities of COVID-19
infection. A novel detection Convolutional Neural Network (CNN), STM-BRNet, is
developed that incorporates the Split-Transform-Merge (STM) block and channel
boosting (CB) to identify COVID-19 infected CT slices in the first stage. Each
STM block extracts boundary and region-smoothing-specific features for COVID-19
infection detection. Moreover, the various boosted channels are obtained by
introducing the new CB and Transfer Learning (TL) concept in STM blocks to
capture small illumination and texture variations of COVID-19-specific images.
The COVID-19 CTs are provided with new SA-CB-BRSeg segmentation CNN for
delineating infection in images in the second stage. SA-CB-BRSeg methodically
utilized smoothening and heterogeneous operations in the encoder and decoder to
capture simultaneously COVID-19 specific patterns that are region homogeneity,
texture variation, and boundaries. Additionally, the new CB concept is
introduced in the decoder of SA-CB-BRSeg by combining additional channels using
TL to learn the low contrast region. The proposed STM-BRNet and SA-CB-BRSeg
yield considerable achievement in accuracy: 98.01 %, Recall: 98.12%, F-score:
98.11%, and Dice Similarity: 96.396%, IOU: 98.845 % for the COVID-19 infectious
region, respectively. The proposed two-stage framework significantly increased
performance compared to single-phase and other reported systems and reduced the
burden on the radiologists.
