Automatic X-ray COVID-19 Lung Image Classification System based on Multi-Level Thresholding and Support Vector Machine

Aboul Ella Hassanien; Lamia Nabil Mahdy; Kadry Ali Ezzat; Haytham H. Elmousalami; Hassan Aboul Ella

The gold standard test to detect the COVID-19 confirmed cases is quantative reverse transcriptase PCR (qRT-PCR). However, this approach needs a CDC guide's sample collection, qualified microbiology expertise, time from 4hrs up to 6hrs. Therefore, medical imaging is very important candidate in screening for the COVID-19 cases [17] . Radiologist's diagnosis involves computed tomography (CT) scans, chest X-ray (CXR) radiographs [10] . COVID-19 symptoms can be effectively detected using CT or X-ray images. Based on the chest CT scans During recovery, radiologists can detect the (COVID-19) pneumonia and the stage of patient recovery or deterioration. Automated artificial intelligence models can accurately provide early detection for the diagnosis of the cases of COVID-19 by detecting the early lung damage signs in the images. An inception artificial neural networks (ANNs) have been applied for binary classification for infected with COVID-19 or health persons using 1,119 CT images [18] . The modified the Inception transfer-learning model produced accuracy of 89.5% with 0.88 and 0.87 for specificity and sensitivity, respectively using internal validation dataset. On the other hand, model produced accuracy of 79.3% with 0.83 and 0.67for specificity and sensitivity, respectively using external validation dataset. Therefore, the findings demonstrate the reliability of deep learning to find COVID-19 radiological features based on terms of time and accuracy.