Kumar Santosh, Gupta Sachin Kumar, Kumar Vinit, Kumar Manoj, Chaube Mithilesh Kumar, Naik Nenavath Srinivas
Department of Computer Science and Engineering, International Institute of Information Technology (IIIT)-Naya Raipur, Chhattisgarh, 4933661, India.
School of Electrical and Communication Engineering, Shri Mata Vaishno Devi University, Katra J&K, India.
Comput Electr Eng. 2022 Oct;103:108396. doi: 10.1016/j.compeleceng.2022.108396. Epub 2022 Sep 20.
Over the past few years, the awful COVID-19 pandemic effect has become a lethal sickness. The processing of the gathered samples requires extra time due to the use of medical diagnostic equipment, methodologies, and clinical testing procedures for the early diagnosis of infected individuals. An innovative multimodal paradigm for the early diagnosis and precise categorization of COVID-19 is put up as a solution to this issue. To extract distinguishing features from the prepared chest X-ray picture and cough (audio) database, chest X-ray-based and cough-based model are used here. Other public chest X-ray image datasets, and the Coswara cough (audio) dataset containing 92 COVID-19 positive, and 1079 healthy subjects (people) using the deep Uniform-Net, and Convolutional Neural Network (CNN). The weighted sum-rule fusion method and ensemble deep learning algorithms are utilized to further combine the extracted features. For the early diagnosis of patients, the framework offers an accuracy of 98.67%.
在过去几年里，可怕的新冠疫情造成了一种致命疾病。由于使用医疗诊断设备、方法以及临床检测程序来对感染者进行早期诊断，处理收集到的样本需要额外时间。作为解决这一问题的方案，提出了一种用于新冠早期诊断和精确分类的创新多模态范例。为了从准备好的胸部X光图像和咳嗽（音频）数据库中提取显著特征，这里使用了基于胸部X光的模型和基于咳嗽的模型。利用深度均匀网络和卷积神经网络（CNN），结合其他公共胸部X光图像数据集以及包含92名新冠阳性患者和1079名健康受试者的科斯瓦拉咳嗽（音频）数据集。采用加权求和规则融合方法和集成深度学习算法来进一步合并提取的特征。对于患者的早期诊断，该框架的准确率为98.67%。
