Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Centre of Artificial Intelligence for Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
J Healthc Eng. 2022 Mar 30;2022:6074538. doi: 10.1155/2022/6074538. eCollection 2022.
Early and accurate detection of COVID-19 is an essential process to curb the spread of this deadly disease and its mortality rate. Chest radiology scan is a significant tool for early management and diagnosis of COVID-19 since the virus targets the respiratory system. Chest X-ray (CXR) images are highly useful in the effective detection of COVID-19, thanks to its availability, cost-effective means, and rapid outcomes. In addition, Artificial Intelligence (AI) techniques such as deep learning (DL) models play a significant role in designing automated diagnostic processes using CXR images. With this motivation, the current study presents a new Quantum Seagull Optimization Algorithm with DL-based COVID-19 diagnosis model, named QSGOA-DL technique. The proposed QSGOA-DL technique intends to detect and classify COVID-19 with the help of CXR images. In this regard, the QSGOA-DL technique involves the design of EfficientNet-B4 as a feature extractor, whereas hyperparameter optimization is carried out with the help of QSGOA technique. Moreover, the classification process is performed by a multilayer extreme learning machine (MELM) model. The novelty of the study lies in the designing of QSGOA for hyperparameter optimization of the EfficientNet-B4 model. An extensive series of simulations was carried out on the benchmark test CXR dataset, and the results were assessed under different aspects. The simulation results demonstrate the promising performance of the proposed QSGOA-DL technique compared to recent approaches.
早期、准确地检测到 COVID-19 对于遏制这种致命疾病的传播及其死亡率至关重要。胸部放射学扫描是 COVID-19 早期管理和诊断的重要工具，因为病毒针对的是呼吸系统。胸部 X 光（CXR）图像在有效检测 COVID-19 方面非常有用，这要归功于其可用性、成本效益手段和快速结果。此外，人工智能（AI）技术，如深度学习（DL）模型，在使用 CXR 图像设计自动化诊断过程方面发挥着重要作用。出于这个动机，本研究提出了一种新的基于量子海鸥优化算法与深度学习的 COVID-19 诊断模型，称为 QSGOA-DL 技术。该提出的 QSGOA-DL 技术旨在借助 CXR 图像检测和分类 COVID-19。在这方面，QSGOA-DL 技术涉及设计高效网络-B4 作为特征提取器，而超参数优化则借助 QSGOA 技术进行。此外，分类过程由多层极限学习机（MELM）模型执行。该研究的新颖之处在于设计 QSGOA 来优化高效网络-B4 模型的超参数。在基准测试 CXR 数据集上进行了广泛的模拟系列，并且从不同方面评估了结果。模拟结果表明，与最近的方法相比，所提出的 QSGOA-DL 技术具有有前景的性能。
