<strong>ملخص الدراسة:</strong>

Based on the best published research from Stanford University, the CheXNet algorithm was developed to diagnose and detect pneumonia from chest X-rays. To achieve better performance than experienced radiologists from the same university, simple changes were made to the algorithm to diagnose 14 pathological condition in the chest X-ray with a performance that exceeds all Previously developed deep learning [1]. In this paper, we experimented with applying a convolutional neural networks (CNN) algorithm in a similar way to the mechanism of work in CheXNet algorithm by using a dataset of 550 Chest X-ray images collected from Kaggle website, some of them are infected with Covid-19 virus. We had an acceptable prediction accuracy of 89.7% which is closed to the results of CheXNet algorithm.

<strong>توثيق المرجعي (APA)</strong>

ملخص الدراسة:

Based on the best published research from Stanford University, the CheXNet algorithm was developed to diagnose and detect pneumonia from chest X-rays. To achieve better performance than experienced radiologists from the same university, simple changes were made to the algorithm to diagnose 14 pathological condition in the chest X-ray with a performance that exceeds all Previously developed deep learning [1]. In this paper, we experimented with applying a convolutional neural networks (CNN) algorithm in a similar way to the mechanism of work in CheXNet algorithm by using a dataset of 550 Chest X-ray images collected from Kaggle website, some of them are infected with Covid-19 virus. We had an acceptable prediction accuracy of 89.7% which is closed to the results of CheXNet algorithm.

توثيق المرجعي (APA)