IEEE Trans Neural Netw Learn Syst. 2024 Mar;35(3):3912-3926. doi: 10.1109/TNNLS.2022.3201198. Epub 2024 Feb 29.
Corona virus disease 2019 is an extremely fatal pandemic around the world. Intelligently recognizing X-ray chest radiography images for automatically identifying corona virus disease 2019 from other types of pneumonia and normal cases provides clinicians with tremendous conveniences in diagnosis process. In this article, a deep ensemble dynamic learning network is proposed. After a chain of image preprocessing steps and the division of image dataset, convolution blocks and the final average pooling layer are pretrained as a feature extractor. For classifying the extracted feature samples, two-stage bagging dynamic learning network is trained based on neural dynamic learning and bagging algorithms, which diagnoses the presence and types of pneumonia successively. Experimental results manifest that using the proposed deep ensemble dynamic learning network obtains 98.7179% diagnosis accuracy, which indicates more excellent diagnosis effect than existing state-of-the-art models on the open image dataset. Such accurate diagnosis effects provide convincing evidences for further detections and treatments.
新型冠状病毒病 2019 是全球范围内一种极其致命的大流行病。通过智能识别 X 射线胸部摄影图像，自动将新型冠状病毒病 2019 与其他类型的肺炎和正常病例区分开来，为临床医生的诊断过程提供了极大的便利。本文提出了一种深度集成动态学习网络。经过一系列图像预处理步骤和图像数据集的划分，卷积块和最终的平均池化层被预训练为特征提取器。对于提取特征样本的分类，基于神经动态学习和装袋算法训练了两阶段装袋动态学习网络，依次诊断肺炎的存在和类型。实验结果表明，使用所提出的深度集成动态学习网络可以获得 98.7179%的诊断准确率，这表明在开放图像数据集上，该模型比现有的最先进模型具有更好的诊断效果。这种准确的诊断效果为进一步的检测和治疗提供了令人信服的证据。
