CoVSeverity-Net: an efficient deep learning model for COVID-19 severity estimation from Chest X-Ray images

Classification of Chest Radiology Images in Order to Identify Patients with COVID-19 Using Deep Learning Techniques

Background and Aim: Due to the important role of radiological images for identifying patients with COVID-19, creating a model based on deep learning methods was the main objective of this study. Materials and Methods: 15,153 available chest images of normal, COVID-19, and pneumonia individuals which were in the Kaggle data repository was used as dataset of this research. Data preprocessing inc...

Using deep learning on X-ray images to detect threats

Human identification using chest x-ray images

This paper proposes a new domain of human identification based on the classification of chest x-ray images. Like fingerprint, retina or face images have been used for human identification, the question whether chest x-ray images can provide valid, accurate and reliable information about the identity of an individual has not been investigated so far and is thus proposed here. Various approaches ...

An abnormal chest x-ray.

A 51-YEAR-OLD white man with a 45-year history of smoking was noted to have an abnormality on a routine chest x-ray. He denied any history of cough, sputum production, chest pain, dyspnea, weight loss, weakness, or tuberculosis. There was no previous chest x-ray available for comparison. The patient’s physical examination results were unremarkable. His chest x-ray is shown in Figure 1 and a com...

Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification

The increased availability of X-ray image archives (e.g. the ChestX-ray14 dataset from the NIH Clinical Center) has triggered a growing interest in deep learning techniques. To provide better insight into the different approaches, and their applications to chest X-ray classification, we investigate a powerful network architecture in detail: the ResNet-50. Building on prior work in this domain, ...