COVID-19 is a serious ongoing worldwide pandemic. Using X-ray chest radiography images for automatically diagnosing COVID-19 is an effective and convenient means of providing diagnostic assistance to clinicians in practice. This paper proposes a bagging dynamic deep learning network (B-DDLN) for diagnosing COVID-19 by intelligently recognizing its symptoms in X-ray chest radiography images. After a series of preprocessing steps for images, we pre-train convolution blocks as a feature extractor. For the extracted features, a bagging dynamic learning network classifier is trained based on neural dynamic learning algorithm and bagging algorithm. B-DDLN connects the feature extractor and bagging classifier in series. Experimental results verify that the proposed B-DDLN achieves 98.8889% testing accuracy, which shows the best diagnosis performance among the existing state-of-the-art methods on the open image set. It also provides evidence for further detection and treatment.
【저자키워드】 Diseases, Computational science, 【초록키워드】 COVID-19, Treatment, deep learning, diagnostic, Diagnosis, Symptom, X-ray, Features, Accuracy, Algorithm, Chest radiography, Evidence, Convolution, open, best, Classifier, clinician, worldwide pandemic, experimental results, diagnosing, block, effective, provide, recognizing, Experimental result, automatically, 【제목키워드】 COVID-19, deep learning, diagnosing,