论文标题
COVID-19使用新通道增强CNN从肺CT图像进行检测和分析
COVID-19 Detection and Analysis From Lung CT Images using Novel Channel Boosted CNNs
论文作者
论文摘要
2019年12月，中国武汉的全球大流行Covid-19影响了人类的生活和全球经济。因此，需要有效的诊断系统来控制其传播。然而，自动诊断系统以有限的标记数据，较小的对比度变化以及感染和背景之间的高结构相似性提出了挑战。在这方面，提出了一种新的两阶段深度卷积神经网络（CNN）的诊断系统来检测微小的不规则性并分析COVID-19的感染。在第一阶段，开发了一种新型的SB-STM-BRNET CNN，并结合了新的通道挤压和增强（SB），并基于卷积的基于卷积的分裂转换 - 块（STM）块，以检测COVID-19的感染肺CT图像。新的STM块执行了多路径区域平滑和边界操作，这有助于学习较小的对比度变化和全球COVID-19的特定模式。此外，使用SB和在STM块中转移学习概念来实现不同的增强通道，以学习Covid-19特异性图像和健康图像之间的纹理变化。在第二阶段，向新型的Covid-CB-Reseg-Reseg-Rese分割CNN提供了COVID-19的感染图像，以识别和分析COVID-19的感染区域。拟议的Covid-CB-Reseg有条不紊地采用了每个编码器模块中的区域均匀性和异质性操作，并使用辅助通道可以同时学习COVID-19受感染区域的低照明和边界。提出的诊断系统在准确性方面产生良好的性能：98.21％，F-评分：98.24％，骰子相似性：96.40％，IOU：COVID-19受感染区域的98.85％。拟议的诊断系统将减轻负担，并加强放射科医生对快速准确的COVID-19诊断的决定。
In December 2019, the global pandemic COVID-19 in Wuhan, China, affected human life and the worldwide economy. Therefore, an efficient diagnostic system is required to control its spread. However, the automatic diagnostic system poses challenges with a limited amount of labeled data, minor contrast variation, and high structural similarity between infection and background. In this regard, a new two-phase deep convolutional neural network (CNN) based diagnostic system is proposed to detect minute irregularities and analyze COVID-19 infection. In the first phase, a novel SB-STM-BRNet CNN is developed, incorporating a new channel Squeezed and Boosted (SB) and dilated convolutional-based Split-Transform-Merge (STM) block to detect COVID-19 infected lung CT images. The new STM blocks performed multi-path region-smoothing and boundary operations, which helped to learn minor contrast variation and global COVID-19 specific patterns. Furthermore, the diverse boosted channels are achieved using the SB and Transfer Learning concepts in STM blocks to learn texture variation between COVID-19-specific and healthy images. In the second phase, COVID-19 infected images are provided to the novel COVID-CB-RESeg segmentation CNN to identify and analyze COVID-19 infectious regions. The proposed COVID-CB-RESeg methodically employed region-homogeneity and heterogeneity operations in each encoder-decoder block and boosted-decoder using auxiliary channels to simultaneously learn the low illumination and boundaries of the COVID-19 infected region. The proposed diagnostic system yields good performance in terms of accuracy: 98.21 %, F-score: 98.24%, Dice Similarity: 96.40 %, and IOU: 98.85 % for the COVID-19 infected region. The proposed diagnostic system would reduce the burden and strengthen the radiologist's decision for a fast and accurate COVID-19 diagnosis.
