A cognitive deep learning approach for medical image processing.

In ophthalmic diagnostics, achieving precise segmentation of retinal blood vessels is a critical yet challenging task, primarily due to the complex nature of retinal images. The intricacies of these images often hinder the accuracy and efficiency of segmentation processes. To overcome these challenges, we introduce the cognitive DL retinal blood vessel segmentation (CoDLRBVS), a novel hybrid model that synergistically combines the deep learning capabilities of the U-Net architecture with a suite of advanced image processing techniques. This model uniquely integrates a preprocessing phase using a matched filter (MF) for feature enhancement and a post-processing phase employing morphological techniques (MT) for refining the segmentation output. Also, the model incorporates multi-scale line detection and scale space methods to enhance its segmentation capabilities. Hence, CoDLRBVS leverages the strengths of these combined approaches within the cognitive computing framework, endowing the system with human-like adaptability and reasoning. This strategic integration enables the model to emphasize blood vessels, accurately segment effectively, and proficiently detect vessels of varying sizes. CoDLRBVS achieves a notable mean accuracy of 96.7%, precision of 96.9%, sensitivity of 99.3%, and specificity of 80.4% across all of the studied datasets, including DRIVE, STARE, HRF, retinal blood vessel and Chase-DB1. CoDLRBVS has been compared with different models, and the resulting metrics surpass the compared models and establish a new benchmark in retinal vessel segmentation. The success of CoDLRBVS underscores its significant potential in advancing medical image processing, particularly in the realm of retinal blood vessel segmentation.

The Impact of the COVID-19 Pandemic and Emergency Distance Teaching on the Psychological Status of University Teachers: A Cross-Sectional Study in Jordan.

The COVID-19 pandemic has struck many countries globally. Jordan has implemented strict nationwide control measures to halt the viral spread, one of which was the closure of universities and shifting to remote teaching. The impact of this pandemic could extend beyond the risk of physical harm to substantial psychological consequences. Our study aimed at assessing 1) psychological status, 2) challenges of distance teaching, and 3) coping activities and pandemic-related concerns among university teachers in Jordan in the midst of COVID-19-related quarantine and control measures. We conducted a cross-sectional study using an anonymous online survey. The measure of psychological distress was obtained using a validated Arabic version of the Kessler Distress Scale (K10). Other information collected included sociodemographic profile, methods used to handle distress, motivation to participate in distance teaching, and challenges of distance teaching as well as the most worrisome issues during this pandemic. Three hundred eighty-two university teachers returned completed surveys. Results of K10 showed that 31.4% of respondents had severe distress and 38.2% had mild to moderate distress. Whereas gender was not associated with distress severity, age had a weak negative correlation (Rho = -0.19, P < 0.0001). Interestingly, most teachers had moderate to high motivation for distance teaching. Engagement with family was the most reported self-coping activity. More than half of the participants were most concerned and fearful about SARS-CoV-2 infection. In conclusion, university teachers have shown to exhibit various levels of psychological distress and challenges during the implementation of precautionary national measures in the battle against COVID-19 in Jordan.