COVID-19 Detection on X-Ray Images using a Combining Mechanism of Pre-trained CNNs

The COVID-19 infection was sparked by the severe acute respiratory syndrome SARS-CoV-2, as mentioned World Health Organization, and originated in Wuhan, Republic of China, eventually extending to every nation worldwide 2020. This research aims establish an efficient Medical Diagnosis Support System (MDSS) for recognizing chest radiography with X-ray data. To build ever more classifier, this MDSS employs concatenation mechanism merge pretrained convolutional neural networks (CNNs) predicated on Transfer Learning (TL) classifiers. In feature extraction phase, proposed classifier a parallel deep approach based Deep (DL). As result, increases accuracy our model, thus identifying cases higher accuracy. suggested trained validated using Chest Radiography image database four categories: COVID-19, Normal, Pneumonia, Tuberculosis during research. Furthermore, we integrated separate public X-Ray imaging datasets construct dataset. contrast, achieved 99.66% 99.48% sensitivity respectively.