Real-Time Multi-Class Infection Classification for Respiratory Diseases

Abstract

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 patients in the shortest amount of time, radiologists can also be used or published online to assist any less-experienced individual in obtaining an accurate immediate screening for their radiological images.