Rajinikanth Venkatesan, Biju Roshima, Mittal Nitin, Mittal Vikas, Askar S S, Abouhawwash Mohamed
Department of Computer Science and Engineering, Division of Research and Innovation, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, Tamil Nadu, India.
Department of Computer Science Engineering, Parul University, Vadodara, 391760, Gujarat, India.
Heliyon. 2024 Mar 2;10(5):e27509. doi: 10.1016/j.heliyon.2024.e27509. eCollection 2024 Mar 15.
Several deep-learning assisted disease assessment schemes (DAS) have been proposed to enhance accurate detection of COVID-19, a critical medical emergency, through the analysis of clinical data. Lung imaging, particularly from CT scans, plays a pivotal role in identifying and assessing the severity of COVID-19 infections. Existing automated methods leveraging deep learning contribute significantly to reducing the diagnostic burden associated with this process. This research aims in developing a simple DAS for COVID-19 detection using the pre-trained lightweight deep learning methods (LDMs) applied to lung CT slices. The use of LDMs contributes to a less complex yet highly accurate detection system. The key stages of the developed DAS include image collection and initial processing using Shannon's thresholding, deep-feature mining supported by LDMs, feature optimization utilizing the Brownian Butterfly Algorithm (BBA), and binary classification through three-fold cross-validation. The performance evaluation of the proposed scheme involves assessing individual, fused, and ensemble features. The investigation reveals that the developed DAS achieves a detection accuracy of 93.80% with individual features, 96% accuracy with fused features, and an impressive 99.10% accuracy with ensemble features. These outcomes affirm the effectiveness of the proposed scheme in significantly enhancing COVID-19 detection accuracy in the chosen lung CT database.
为了通过临床数据分析更准确地检测新型冠状病毒肺炎（COVID-19）这一重大医疗急症，已经提出了几种深度学习辅助疾病评估方案（DAS）。肺部成像，尤其是CT扫描成像，在识别和评估COVID-19感染的严重程度方面起着关键作用。现有的利用深度学习的自动化方法对减轻这一过程中的诊断负担有很大帮助。本研究旨在开发一种简单的DAS，用于使用应用于肺部CT切片的预训练轻量级深度学习方法（LDM）检测COVID-19。LDM的使用有助于构建一个不太复杂但高度准确的检测系统。所开发的DAS的关键阶段包括使用香农阈值法进行图像采集和初始处理、由LDM支持的深度特征挖掘、利用布朗蝴蝶算法（BBA）进行特征优化以及通过三倍交叉验证进行二元分类。对所提出方案的性能评估包括评估单个特征、融合特征和整体特征。调查显示，所开发的DAS使用单个特征时检测准确率达到93.80%，使用融合特征时准确率为96%，使用整体特征时准确率高达99.10%。这些结果证实了所提出方案在显著提高所选肺部CT数据库中COVID-19检测准确率方面的有效性。
