Determination of Covid-19 Pulmonary Infection Rate From X-Ray Images Using U-Net Model - Mohammed Al-Tamimi; Hadeel Jabar; Husam Ali Abdulmohsin; Farah khiled AL-Jibory (Journal of Applied Engineering and Technological Science)
Global health has suffered by millions of COVID-19 infections and fatalities. Pneumonia and ARDS are major consequences of this viral infection. Patient treatment and resource allocation depend on accurate lung infection rates. Reverse transcription polymerase chain reaction (RT-PCR) is highly specific but lacks sensitivity, especially in early infection. Thus, imaging, particularly chest X-rays, is crucial for detecting and monitoring COVID-19-related pulmonary problems. Among various image processing techniques, deep learning methods, especially U-net models, have shown promising results in segmenting and analyzing X-ray images to determine the extent of lung infection. This article explores the importance of imaging techniques in diagnosing COVID-19 lung infection, provides an overview of the U-Net model in medical imaging, and describes in detail the method of using this complex model to determine infection rates from radiographs. This study discusses the diagnosis of coronavirus infection, i.e. whether a person is infected or not, and determines the infection rate (severity) that mean the percentage of virus in the lungs was calculated based on a global radiograph (X-ray) dataset to study the infection, infection rate, and diagnosis by specialists using a pre-trained model (U-Net model). The results obtained were 97% accurate in diagnosing whether a patient was infected with the virus or not.