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.

翻译：COVID-19是一种新型病原体，于2019年末首次在人类群体中出现，感染后可能引发新型变异肺炎。作为一种快速传播的传染病，COVID-19对人类的感染速度较快。因此，高效的诊断系统能够准确识别感染患者，从而有助于控制其传播。为此，本文开发了一种新的两阶段分析框架，用于分析COVID-19感染的细微异常。在第一阶段，提出了一种新型检测卷积神经网络（CNN）——STM-BRNet，该网络融合了分割-变换-合并（STM）模块和通道增强（CB）技术，用于识别COVID-19感染的CT切片。每个STM模块提取针对COVID-19感染检测的边界和平滑区域特定特征。此外，通过在STM模块中引入新型CB与迁移学习（TL）概念，获取多个增强通道，以捕捉COVID-19特定图像中的微小光照和纹理变化。在第二阶段，采用新型SA-CB-BRSeg分割CNN对COVID-19 CT图像进行感染区域描绘。SA-CB-BRSeg方法系统地在编码器和解码器中运用平滑与异构操作，同时捕捉COVID-19特定模式，包括区域均匀性、纹理变化和边界。同时，在SA-CB-BRSeg的解码器中引入新型CB概念，通过TL组合额外通道以学习低对比度区域。所提出的STM-BRNet和SA-CB-BRSeg在COVID-19感染区域表现优异：准确率98.01%、召回率98.12%、F值98.11%、Dice相似度96.396%、交并比98.845%。该两阶段框架相较于单阶段及其他现有系统显著提升了性能，并减轻了放射科医师的负担。