COVID-19 Diagnosis Using X-ray Images Based on Convolutional Neural Networks

Abstract

Coronavirus (COVID-19) is considered as a viral disease that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Spreading of COVID-19 will continue to affect health and economics. Chest X-ray and CT imaging techniques are crucial for infected patients in the battle with COVID-19. Recently, Convolutional Neural Network has been considered as a type of deep learning tools, and it can be used for detecting diseases such as COVID-19. This paper introduces an efficient architecture for COVID-19 diagnosis from an X-ray dataset. The proposed architecture starts with image pre-processing using lung segmentation and image resizing. Deep feature extraction is performed using the proposed CNN model and different pre-trained models. The classification process is performed using either a Support Vector Machine (SVM) or a Softmax classifier. Simulation results prove that the proposed model can classify COVID-19 images with high accuracies of 98.7% and 98.5% for SVM and Softmax classifiers, respectively. The performance metrics are the processing time,

A Novel Hybrid Machine Learning Based System to Classify Shoulder Implant Manufacturers

“…Later, this idea evolved into using neural networks with various ML classifiers. In the field of medical image processing, this method has been used in many studies, such as in the diagnosis of skin cancer [ 22 ], the classification of mammogram images [ 23 ], the early diagnosis of dementia [ 24 ], the diagnosis of COVID-19 disease [ 25 ], and the diagnosis of lung diseases [ 26 ], and successful results have been obtained. The hybrid methods proposed in this study are mainly inspired by a study conducted by Tang [ 21 ].…”