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Objective: The first imaging method to play an vital role in the diagnosis of COVID-19 illness is the chest X-ray. Because of the abundance of large-scale annotated picture datasets, convolutional neural networks (CNN) have shown remarkable success in image recognition and classification. The current study aims to construct a successful deep learning model that can distinguish COVID-19 from healthy controls using chest X-ray images. Material and Methods: The dataset in the study consists of subjects with 912 negative and 912 positive PCR results. A prediction model was built using VGG-16 with transfer learning to classify COVID-19 chest X-ray images. The data set was split at random into 80 % training and 20% testing groups. Results: The accuracy, F1 score, sensitivity, specificity, positive and negative values from the model that can successfully distinguish COVID-19 from healthy controls are 97.3%, 97.3%, 97.8%, 96.7%, 96.7%, and 97.8% regarding the testing dataset, respectively. Conclusion: The suggested technique might greatly improve on current radiology-based methodologies and serve as a beneficial tool for clinicians/radiologists in diagnosing and following up on COVID-19 patients.

A 49-year old man who was sent to our hospital because of high fever, cellulitis at left arm and pancytopenia was diagnosed as neutropenic fever, and treatment with imipenem 4 x 500 mg intravenous (IV) and clindamycin 4 x 600 mg IV was started immediately after the cultures were taken. The patient had been followed up for type 2 diabetes mellitus for ten years, and after our clinical and laboratory examinations he was diagnosed as acute myeloid leukemia (AML). Aeromonas veronii biovar sobria was isolated as the agent of cellulitis at the left arm after trauma. As the microorganism was sensitive to imipenem, treatment according to antimicrobial susceptibility test, the present treatment was continued. The patient whose general condition worsened at the follow up died despite all supportive treatment.