Identification of Tuberculosis and Coronavirus Patients Using Hybrid Deep Learning Models

作     者：Mohammed A.Al Ghamdi 

作者机构：Computer Science DepartmentUmm Al-Qura UniversityMakkah City24243Saudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第76卷第7期

页      面：881-894页

核心收录：

学科分类：1007[医学-药学(可授医学、理学学位)] 100705[医学-微生物与生化药学] 1001[医学-基础医学(可授医学、理学学位)] 100103[医学-病原生物学] 10[医学] 

基　　金：Data and Artificial Intelligence Scientific Chair Umm Al-Qura University, UQU 

主　　题：Tuberculosis coronavirus X-ray deep learning VGG16 and ResNet18 

摘      要：Considerable resources,technology,and efforts are being utilized worldwide to eradicate the *** certain measures taken to prevent the further spread of the disease have been successful,efforts to completely wipe out the coronavirus have been *** patients have symptoms similar to those of chest Tuberculosis(TB)or pneumonia *** tuberculosis and coronavirus are similar because both diseases affect the lungs,cause coughing and produce an irregular respiratory *** diseases can be confirmed through X-ray *** is a difficult task to diagnose COVID-19,as coronavirus testing kits are neither excessively available nor very *** addition,specially trained staff and specialized equipment in medical laboratories are needed to carry out a coronavirus ***,most of the staff is not fully trained,and several laboratories do not have special equipment to perform a coronavirus ***,hospitals and medical staff are under stress to meet necessary *** of the time,these staffs confuse the tuberculosis or pneumonia patient with a coronavirus patient,as these patients present similar *** meet the above challenges,a comprehensive solution based on a deep learning model has been proposed to distinguish COVID-19 patients from either tuberculosis patients or healthy *** framework contains a fusion of Visual Geometry Group from Oxford(VGG16)and Residual Network(ResNet18)algorithms as VGG16 contains robust convolutional layers,and Resnet18 is a good *** proposed model outperforms other machine learning and deep learning models as more than 94%accuracy for multiclass identification has been achieved.