Image processing and artificial neural network based determination of surface mean texture depth on lab-controlled chip seal pavement samples.

Because surface texture is nearly the sole indicator of pavement functional properties and highly correlated with critical operational characteristics of roadways like traffic noise and safety, the change in pavement surface texture because of traffic loadings and environment has to be evaluated routinely. There are numerous direct or indirect evaluation techniques in the market. However, most of these methods have some limitations like requiring lane closure or being expensive.

COVID-19 diagnosis from chest X-ray images using transfer learning: Enhanced performance by debiasing dataloader.

Background: Chest X-ray imaging has been proved as a powerful diagnostic method to detect and diagnose COVID-19 cases due to its easy accessibility, lower cost and rapid imaging time.

Objective: This study aims to improve efficacy of screening COVID-19 infected patients using chest X-ray images with the help of a developed deep convolutional neural network model (CNN) entitled nCoV-NET.

Methods: To train and to evaluate the performance of the developed model, three datasets were collected from resources of "ChestX-ray14", "COVID-19 image data collection", and "Chest X-ray collection from Indiana University," respectively.