COV-CTX: A Deep Learning Approach to Detect COVID-19 from Lung CT and X-Ray Images

Using deep learning on X-ray images to detect threats

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...

COVID-19: a hypothesis to prevent SARS-CoV-2 from entering respiratory cells

Coronaviruses (CoVs) are a group of viruses that induce infection in the respiratory and other systems in the human body. There are two coronaviruses that transmitted from animals to humans including severe acute respiratory syndrome (SARS) and the Middle East respiratory syndrome (MERS) (1). The novel coronavirus that appeared at first in Wuhan, China, in December 2019 was named as severe acut...

Mining Lung Shape from X-Ray Images

This paper presents an approach for mining 2D shape of human lungs from large x-ray image archives of a national level. Images were accumulated in framework of a compulsory computerized country-wide screening programme launched few years ago which is being under development. Three study groups of images containing about 21, 18 and 39 thousand of subjects were created by sub-sampling from a test...

A Novel Approach to Detect Fissures in Lung CT Images using Marker-based Watershed Transformation

Detection and segmentation of fissures is useful in the clinical interpretation of CT lung images to diagnose the presence of pathologies in the human lungs. A new automated method based on marker-based watershed transformation has been proposed to segment the fissures considering its unique structure as a long connected component. Marker based watershed transformation is applied and morphologi...