Coronavirus disease 2019 (COVID-19) is a global pandemic posing significant health risks. The diagnostic test sensitivity of COVID-19 is limited due to irregularities in specimen handling. We propose a deep learning framework that identifies COVID-19 from medical images as an auxiliary testing method to improve diagnostic sensitivity. We use pseudo-coloring methods and a platform for annotating X-ray and computed tomography images to train the convolutional neural network, which achieves a performance similar to that of experts and provides high scores for multiple statistical indices (F1 scores > 96.72% (0.9307, 0.9890) and specificity >99.33% (0.9792, 1.0000)). Heatmaps are used to visualize the salient features extracted by the neural network. The neural network-based regression provides strong correlations between the lesion areas in the images and five clinical indicators, resulting in high accuracy of the classification framework. The proposed method represents a potential computer-aided diagnosis method for COVID-19 in clinical practice. Liang, Gu and other colleagues develop a convoluted neural network (CNN)-based framework to diagnose COVID-19 infection from chest X-ray and computed tomography images, and comparison with other upper respiratory infections. Compared to expert evaluation of the images, the neural network achieved upwards of 99% specificity, showing promise for the automated detection of COVID-19 infection in clinical settings.
【저자키워드】 Infectious diseases, Diseases, Computational biology and bioinformatics, Image processing, 【초록키워드】 COVID-19, coronavirus disease, respiratory infections, Coronavirus disease 2019, deep learning, diagnostic test, diagnostic, Diagnosis, global pandemic, Computed tomography, X-ray, sensitivity, specificity, Health, infections, COVID-19 infection, diagnostic sensitivity, Convolutional neural network, automated, chest X-ray, respiratory, correlation, platform, diagnose, Clinical practice, specimen, clinical settings, promise, handling, high accuracy, Heatmaps, heatmap, FIVE, feature, statistical, upper respiratory, IMPROVE, resulting, identify, develop, provide, auxiliary, 【제목키워드】 COVID-19, automated,