Search

Nonmydriatic retinal fundus images often suffer from quality issues and artifacts due to ocular or systemic comorbidities, leading to potential inaccuracies in clinical diagnoses. In recent times, deep learning methods have been widely employed to improve retinal image quality. However, these methods often require large datasets and lack robustness in clinical settings. Conversely, the inherent stability and adaptability of traditional unsupervised learning methods, coupled with their reduced reliance on extensive data, render them more suitable for real-world clinical applications, particularly in the limited data context of high noise levels or a significant presence of artifacts. However, existing unsupervised learning methods encounter challenges such as sensitivity to noise and outliers, reliance on assumptions like cluster shapes, and difficulties with scalability and interpretability, particularly when utilized for retinal image enhancement. To tackle these challenges, we propose a novel robust PCA (RPCA) method with low-rank sparse decomposition that also integrates affine transformations τi, weighted nuclear norm, and the L2,1 norms, aiming to overcome existing method limitations and to achieve image quality improvement unseen by these methods. We employ the weighted nuclear norm (Lw,∗) to assign weights to singular values to each retinal images and utilize the L2,1 norm to eliminate correlated samples and outliers in the retinal images. Moreover, τi is employed to enhance retinal image alignment, making the new method more robust to variations, outliers, noise, and image blurring. The Alternating Direction Method of Multipliers (ADMM) method is used to optimally determine parameters, including τi, by solving an optimization problem. Each parameter is addressed separately, harnessing the benefits of ADMM. Our method introduces a novel parameter update approach and significantly improves retinal image quality, detecting cataracts, and diabetic retinopathy. Simulation results confirm our method's superiority over existing state-of-the-art methods across various datasets.

Background: World Health Organization recommends exclusive breastfeeding (EBF) for the first 6 months of life. EBF has sustainable long-term health benefits for both infants and mothers. Despite its benefits, the practice of EBF in Ethiopia is lower than the internationally recommended one. This study aimed at identifying factors influencing EBF practice among under-6 month infants in Ethiopia., Methods: This study used data drawn from the 2019 Ethiopian Mini Demographic and Health Survey (2019 EMDHS) data. A multivariable logistic regression model was employed to investigate factors significantly associated with EBF practice among under-6 month infants in Ethiopia. An adjusted odds ratio with 95% confidence interval was used to measure the association of factors with EBF practice., Results: A total of 566 infants under the age of 6 months were included in the study. The prevalence of exclusive breastfeeding practice was 83% (95% CI: 79.70-86%). Urban residences (AOR: 0.40, 95% CI: 0.22-0.73), mothers having secondary education (AOR: 1.54, 95% CI: 1.29-1.84) and higher education (AOR: 3.18, 95% CI: 0.68-15.02), mothers having ANC visits of 1 to 3 times (AOR: 1.52, 95% CI: 1.24-1.88) and ANC visits of 4 and more times (AOR: 4.27, 95% CI: 1.06-17.25), family size of more than 5 (AOR: 0.45, 95% CI: 0.26-0.88), caesarean births (AOR: 0.63, 95% CI: 0.42-0.95), and deliveries at health facilities (AOR: 2.51, 95% CI: 1.12-5.63) were factors significantly associated with EBF practice among under-6 month infants., Conclusion: In this study, EBF practice among under-6 month infants was significantly associated with place of residence, maternal educational level, ANC visits, family size, mode of delivery, and place of delivery. Therefore, encouraging ANC visit and promotion of institutional (health facility) delivery are recommended. Furthermore, special attention has to be given to mothers with no or less education to make them better aware of the EBF and its benefits to enhance exclusive breastfeeding practice., (© 2022. The Author(s).)

In this study, predictive models are proposed to accurately estimate the confirmed cases and deaths due to of Corona virus 2019 (COVID-19) in Africa. The study proposed the predictive models to determine the spatial and temporal pattern of COVID 19 in Africa. The result of the study has shown that the spatial and temporal pattern of the pandemic is varying across in the study area. The result has shown that cubic model is best outperforming compared to the other six families of exponentials ( R 2 = 0.996 , F = 538.334 , D F 1 = 3 , D F 1 = 7 , b 1 = 13691.949 , b 2 = - 824.701 , b 1 = 12.956 ) . The adopted cubic algorithm is more robust in predicting the confirmed cases and deaths due to COVID 19. The cubic algorithm is more superior to the state of the art of the works based on the world health organization data. This also entails the best way to mitigate the expansion of COVID 19 is through persistent and strict self-isolation. This pandemic will sustain to grow up, and peak to the highest for which a strong care and public health interventions practically implemented. It is highly recommended for Africans must go beyond theory preparations implementations practically through the public interventions., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Authors. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd.)