Optimizing dual energy X-ray image enhancement using a novel hybrid fusion method.

Background: Airport security is still a main concern for assuring passenger safety and stopping illegal activity. Dual-energy X-ray Imaging (DEXI) is one of the most important technologies for detecting hidden items in passenger luggage. However, noise in DEXI images, arising from various sources such as electronic interference and fluctuations in X-ray intensity, can compromise the effectiveness of object identification.

A genetic programming Rician noise reduction and explainable deep learning model for Alzheimer's diseases severity prediction.

Background: Degradation of magnetic resonance imaging (MRI) remains a challenging issue, with noise being a key damaging component introduced due to a variety of environmental and mechanical factors.

Objective: The aim of this research work is to addresses the issue of noise reduction and to predict Alzheimer's disease detection efficiently.

Methods: First, we present a genetic programming (GP) technique for reducing Rician noise in MRI images to pre-process the dataset.

Brain Tumor MRI Classification Using a Novel Deep Residual and Regional CNN.

Brain tumor classification is essential for clinical diagnosis and treatment planning. Deep learning models have shown great promise in this task, but they are often challenged by the complex and diverse nature of brain tumors. To address this challenge, we propose a novel deep residual and region-based convolutional neural network (CNN) architecture, called Res-BRNet, for brain tumor classification using magnetic resonance imaging (MRI) scans.

COVID-19 infection analysis framework using novel boosted CNNs and radiological images.

COVID-19, a novel pathogen that emerged in late 2019, has the potential to cause pneumonia with unique variants upon infection. Hence, the development of efficient diagnostic systems is crucial in accurately identifying infected patients and effectively mitigating the spread of the disease. However, the system poses several challenges because of the limited availability of labeled data, distortion, and complexity in image representation, as well as variations in contrast and texture.

Prevalence of Coagulation Factors Deficiency among Young Adults in Saudi Arabia: A National Survey.

 Inherited bleeding disorders vary in prevalence due to genetic disparity and ethnicity. Little is known about the prevalence of coagulation factor deficiency and bleeding disorders in middle-eastern population.  Young Saudi adults with at least one positive bleeding symptom reported in semi-structured validated condensed MCMDM-1vWD questionnaire were tested for complete blood count, routine and special coagulation tests, serum ferritin level, and capillary zone electrophoresis.