基于深度CNN和Chimp优化算法稳定的极限学习机的X射线图像实时COVID-19诊断
Real-Time COVID-19 Diagnosis from X-Ray Images Using Deep CNN and Extreme Learning Machines Stabilized by Chimp Optimization Algorithm
摘要：
由于快速诊断COVID-19病例的需求不断增加，使用放射图像实时检测COVID-19已获得优先考虑。本文介绍了一种新的两阶段的方法进行分类的胸部X射线图像。深度学习（DL）方法无法涵盖这些方面，因为训练和微调模型的参数需要花费大量时间。在这种方法中，第一阶段是训练深度CNN作为特征提取器，第二阶段是使用极限学习机（ELM）进行实时检测。ELM的主要缺点是在应用图像处理时需要大量的隐层节点来获得可靠和准确的检测器，因为检测性能显著地依赖于初始权重和偏置的设置。因此，本文使用黑猩猩优化算法（ChOA），以改善结果，并增加网络的可靠性，同时保持实时性。设计的检测器将在COVID-Xray-5 k和COVIDectioNet数据集上进行基准测试，并通过与经典DCNN，遗传算法优化的ELM（GA-ELM），布谷鸟搜索优化的ELM（CS-ELM）和鲸鱼优化算法优化的ELM（WOA-ELM）进行比较来验证结果。所提出的方法优于其他比较基准，在COVID-Xray-5 k和COVIDectioNet数据集上的最终准确率分别为98.25%和99.11%，并且与卷积CNN相比，它导致相对误差减少了1.75%和1.01%。更重要的是，训练深度ChOA-ELM所需的时间仅为0.9474毫秒，3100张图像的整体测试时间为2.937秒。
英文摘要：
Real-time detection of COVID-19 using radiological images has gained priority due to the increasing demand for fast diagnosis of COVID-19 cases. This paper introduces a novel two-phase approach for classifying chest X-ray images. Deep Learning (DL) methods fail to cover these aspects since training and fine-tuning the model's parameters consume much time. In this approach, the first phase comes to train a deep CNN working as a feature extractor, and the second phase comes to use Extreme Learning Machines (ELMs) for real-time detection. The main drawback of ELMs is to meet the need of a large number of hidden-layer nodes to gain a reliable and accurate detector in applying image processing since the detective performance remarkably depends on the setting of initial weights and biases. Therefore, this paper uses Chimp Optimization Algorithm (ChOA) to improve results and increase the reliability of the network while maintaining real-time capability. The designed detector is to be benchmarked on the COVID-Xray-5k and COVIDetectioNet datasets, and the results are verified by comparing it with the classic DCNN, Genetic Algorithm optimized ELM (GA-ELM), Cuckoo Search optimized ELM (CS-ELM), and Whale Optimization Algorithm optimized ELM (WOA-ELM). The proposed approach outperforms other comparative benchmarks with 98.25% and 99.11% as ultimate accuracy on the COVID-Xray-5k and COVIDetectioNet datasets, respectively, and it led relative error to reduce as the amount of 1.75% and 1.01% as compared to a convolutional CNN. More importantly, the time needed for training deep ChOA-ELM is only 0.9474 milliseconds, and the overall testing time for 3100 images is 2.937 seconds.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
