Gaur Loveleen, Bhatia Ujwal, Jhanjhi N Z, Muhammad Ghulam, Masud Mehedi
Amity International Business School, Amity University, Noida, India.
School of Computer Science and Engineering SCE, Taylor's University, Subang Jaya, Malaysia.
Multimed Syst. 2023;29(3):1729-1738. doi: 10.1007/s00530-021-00794-6. Epub 2021 Apr 28.
The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) are evaluated through transfer learning. The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications. The dataset used for the study is publicly available and compiled from different sources. This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores). The results show that the proposed approach produced a high-quality model, with an overall accuracy of 92.93%, COVID-19, a sensitivity of 94.79%. The work indicates a definite possibility to implement computer vision design to enable effective detection and screening measures.
全球对新型冠状病毒（COVID-19）自动检测的需求正在增加。病例的指数级增长给医疗机构带来了负担，人们正在探索大量的多媒体医疗数据以寻找解决方案。本研究提出了一种实用的解决方案，通过深度卷积神经网络（CNN）从胸部X光片中检测COVID-19，同时区分正常情况以及受病毒性肺炎影响的情况。在本研究中，通过迁移学习对三种预训练的CNN模型（EfficientNetB0、VGG16和InceptionV3）进行了评估。选择这些特定模型的理由是它们在准确性和效率之间取得了平衡，且参数较少，适合移动应用。用于该研究的数据集是公开可用的，并且是从不同来源汇编而成的。本研究使用了深度学习技术和性能指标（准确率、召回率、特异性、精确率和F1分数）。结果表明，所提出的方法产生了一个高质量的模型，总体准确率为92.93%，对COVID-19的敏感度为94.79%。这项工作表明实施计算机视觉设计以实现有效检测和筛查措施具有明确的可能性。
