Somaya Al Maadeed

TL;DR: In this article, the effect of image enhancement and lung segmentation of a large dataset in COVID-19 detection was not reported in the literature; however, the proposed approach with very reliable and comparable performance will boost the fast and robust detection of coronavirus disease using chest X-ray images.

TL;DR: An approach with very reliable and comparable performance will boost the fast and robust COVID-19 detection using chest X-ray images and the reliability of network performance is significantly improved for the segmented lung images, which was observed using the visualization technique.

TL;DR: This paper presents a new offline dataset called the Qatar University Writer Identification dataset (QUWI), which consists of handwritten documents of 1017 volunteers and allows the dataset to be used for both text-dependent and text-independent writer identification tasks.

TL;DR: This study proposes several geometric features to characterize handwritings and uses these features to perform the classification of hand Writings with regards to age, gender, and nationality and combines these features using random forests and kernel discriminant analysis.

TL;DR: In this article, a cascaded system was proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography images, which achieved an elegant performance for lung infection segmentation with a Dice Similarity Coefficient (DSC) of 94.13% and IoU of 91.85% using the FPN with DenseNet201 encoder.