Department of the Robotics and Intelligent Machines, Kafrelsheikh University, Kafrelsheikh 33511, Egypt.
Electrical Engineering Department, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.
Viruses. 2020 Jul 16;12(7):769. doi: 10.3390/v12070769.
This generation faces existential threats because of the global assault of the novel Corona virus 2019 (i.e., COVID-19). With more than thirteen million infected and nearly 600000 fatalities in 188 countries/regions, COVID-19 is the worst calamity since the World War II. These misfortunes are traced to various reasons, including late detection of latent or asymptomatic carriers, migration, and inadequate isolation of infected people. This makes detection, containment, and mitigation global priorities to contain exposure via quarantine, lockdowns, work/stay at home, and social distancing that are focused on "flattening the curve". While medical and healthcare givers are at the frontline in the battle against COVID-19, it is a crusade for all of humanity. Meanwhile, machine and deep learning models have been revolutionary across numerous domains and applications whose potency have been exploited to birth numerous state-of-the-art technologies utilised in disease detection, diagnoses, and treatment. Despite these potentials, machine and, particularly, deep learning models are data sensitive, because their effectiveness depends on availability and reliability of data. The unavailability of such data hinders efforts of engineers and computer scientists to fully contribute to the ongoing assault against COVID-19. Faced with a calamity on one side and absence of reliable data on the other, this study presents two data-augmentation models to enhance learnability of the Convolutional Neural Network (CNN) and the Convolutional Long Short-Term Memory (ConvLSTM)-based deep learning models (DADLMs) and, by doing so, boost the accuracy of COVID-19 detection. Experimental results reveal improvement in terms of accuracy of detection, logarithmic loss, and testing time relative to DLMs devoid of such data augmentation. Furthermore, average increases of 4% to 11% in COVID-19 detection accuracy are reported in favour of the proposed data-augmented deep learning models relative to the machine learning techniques. Therefore, the proposed algorithm is effective in performing a rapid and consistent Corona virus diagnosis that is primarily aimed at assisting clinicians in making accurate identification of the virus.
这一代人面临着生存威胁，因为全球正在遭受新型冠状病毒 2019 年（即 COVID-19）的侵袭。在 188 个国家/地区，已有超过 1300 万人感染，近 60 万人死亡，COVID-19 是自第二次世界大战以来最严重的灾难。这些不幸可以追溯到多种原因，包括对潜伏或无症状携带者的检测延迟、迁移以及对感染者的隔离不足。这使得检测、遏制和缓解成为全球优先事项，通过检疫、封锁、居家工作/生活以及社交隔离来限制暴露，重点是“拉平曲线”。虽然医疗保健工作者是抗击 COVID-19 的第一线，但这也是全人类的一场斗争。与此同时，机器和深度学习模型在许多领域和应用中都具有革命性，其强大功能已被用于开发许多用于疾病检测、诊断和治疗的最先进技术。尽管有这些潜力，但机器，尤其是深度学习模型对数据非常敏感，因为其有效性取决于数据的可用性和可靠性。由于缺乏此类数据，工程师和计算机科学家难以充分为抗击 COVID-19 做出贡献。面对灾难，另一方面又缺乏可靠的数据，本研究提出了两种数据增强模型，以增强卷积神经网络（CNN）和基于卷积长短期记忆（ConvLSTM）的深度学习模型（DADLMs）的可学习性，并由此提高 COVID-19 的检测准确性。实验结果表明，与没有数据增强的深度学习模型相比，在检测准确性、对数损失和测试时间方面都有所提高。此外，与机器学习技术相比，报告了 COVID-19 检测准确性平均提高 4%至 11%，有利于所提出的数据增强深度学习模型。因此，所提出的算法在执行快速一致的冠状病毒诊断方面是有效的，主要目的是帮助临床医生准确识别病毒。
