Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Comput Intell Neurosci. 2022 Aug 22;2022:7508836. doi: 10.1155/2022/7508836. eCollection 2022.
The outbreak of the COVID-19 pandemic necessitates prompt identification of affected persons to restrict the spread of the COVID-19 epidemic. Radiological imaging such as computed tomography (CT) and chest X-rays (CXR) is considered an effective way to diagnose COVID-19. However, it needs an expert's knowledge and consumes more time. At the same time, artificial intelligence (AI) and medical images are discovered to be helpful in effectively assessing and providing treatment for COVID-19 infected patients. In particular, deep learning (DL) models act as a vital part of a high-performance classification model for COVID-19 recognition on CXR images. This study develops a heap-based optimization with the deep transfer learning model for detection and classification (HBODTL-DC) of COVID-19. The proposed HBODTL-DC system majorly focuses on the identification of COVID-19 on CXR images. To do so, the presented HBODTL-DC model initially exploits the Gabor filtering (GF) technique to enhance the image quality. In addition, the HBO algorithm with a neural architecture search network (NasNet) large model is employed for the extraction of feature vectors. Finally, Elman Neural Network (ENN) model gets the feature vectors as input and categorizes the CXR images into distinct classes. The experimental validation of the HBODTL-DC model takes place on the benchmark CXR image dataset from the Kaggle repository, and the outcomes are checked in numerous dimensions. The experimental outcomes stated the supremacy of the HBODTL-DC model over recent approaches with a maximum accuracy of 0.9992.
COVID-19 大流行的爆发需要迅速识别受感染者，以限制 COVID-19 疫情的传播。放射影像学，如计算机断层扫描（CT）和胸部 X 光（CXR），被认为是诊断 COVID-19 的有效方法。然而，它需要专家的知识并且耗费更多时间。同时，人工智能（AI）和医学图像被发现有助于有效评估和为 COVID-19 感染患者提供治疗。特别是，深度学习（DL）模型在 CXR 图像上进行 COVID-19 识别的高性能分类模型中发挥着重要作用。本研究开发了一种基于堆的优化与深度迁移学习模型用于检测和分类（HBODTL-DC）的 COVID-19。所提出的 HBODTL-DC 系统主要侧重于 CXR 图像上的 COVID-19 识别。为此，所提出的 HBODTL-DC 模型最初利用 Gabor 滤波（GF）技术来提高图像质量。此外，采用带有神经架构搜索网络（NasNet）大型模型的 HBO 算法来提取特征向量。最后，Elman 神经网络（ENN）模型将特征向量作为输入，并将 CXR 图像分类到不同的类别中。HBODTL-DC 模型的实验验证在 Kaggle 存储库中的基准 CXR 图像数据集上进行，并在多个维度上检查结果。实验结果表明，HBODTL-DC 模型优于最近的方法，具有最高的准确度为 0.9992。
