Real-Time Multi-Class Infection Classification for Respiratory Diseases  

作　　者：Ahmed El.Shafee Walid El-Shafai Abdulaziz Alarifi Mohammed Amoon Aman Singh Moustafa H.Aly 

机构地区：[1]Department of Electrical Engineering,Faculty of Engineering,Ahram Canadian University,Giza,Egypt [2]Department of Electronics and Electrical Communications Engineering,Faculty of Electronic Engineering,Menoufia University,Menouf,32952,Egypt [3]Department of Computer Science,Community College,King Saud University,Riyadh,11437,Saudi Arabia [4]Higher Polytechnic School,Universidad Europea del Atlántico,Santander,39011,Spain [5]Electronics and Communications Engineering Department,College of Engineering and Technology,Arab Academy for Science,Technology and Maritime Transport,Alexandria,1029,Egypt

出　　处：《Computers, Materials & Continua》2022年第11期4157-4177,共21页计算机、材料和连续体（英文）

基　　金：This work was funded by the Researchers Supporting Project Number(RSP-2021/300),King Saud University,Riyadh,Saudi Arabia.

摘　　要：Real-time disease prediction has emerged as the main focus of study in the field of computerized medicine.Intelligent disease identification framework can assist medical practitioners in diagnosing disease in a way that is reliable,consistent,and timely,successfully lowering mortality rates,particularly during endemics and pandemics.To prevent this pandemic’s rapid and widespread,it is vital to quickly identify,confine,and treat affected individuals.The need for auxiliary computer-aided diagnostic(CAD)systems has grown.Numerous recent studies have indicated that radiological pictures contained critical information regarding the COVID-19 virus.Utilizing advanced convolutional neural network(CNN)architectures in conjunction with radiological imaging makes it possible to provide rapid,accurate,and extremely useful susceptible classifications.This research work proposes a methodology for real-time detection of COVID-19 infections caused by the Corona Virus.The purpose of this study is to offer a two-way COVID-19(2WCD)diagnosis prediction deep learning system that is built on Transfer Learning Methodologies(TLM)and features customized fine-tuning on top of fully connected layered pre-trained CNN architectures.2WCD has applied modifications to pre-trained models for better performance.It is designed and implemented to improve the generalization ability of the classifier for binary and multi-class models.Along with the ability to differentiate COVID-19 and No-Patient in the binary class model and COVID-19,No-Patient,and Pneumonia in the multi-class model,our framework is augmented with a critical add-on for visually demonstrating infection in any tested radiological image by highlighting the affected region in the patient’s lung in a recognizable color pattern.The proposed system is shown to be extremely robust and reliable for real-time COVID-19 diagnostic prediction.It can also be used to forecast other lung-related disorders.As the system can assist medical practitioners in diagnosing the greatest number of patie

关 键 词：COVID-19 real-time computerized disease prediction intelligent disease identification framework CAD systems X-rays CT-scans CNN real-time detection of COVID-19 infections 

分 类 号：R56[医药卫生—呼吸系统]