Big Data Framework Using Spark Architecture for Dose Optimization Based on Deep Learning in Medical Imaging

Deep learning and machine learning provide more consistent tools and powerful functions for recognition, classification, reconstruction, noise reduction, quantification and segmentation in biomedical image analysis. Some breakthroughs. Recently, some applications of deep learning and machine learning for low-dose optimization in computed tomography have been developed. Due to reconstruction and processing technology, it has become crucial to develop architectures and/or methods based on deep learning algorithms to minimize radiation during computed tomography scan inspections. This chapter is an extension work done by Alla et al. in 2020 and explain that work very well. This chapter introduces the deep learning for computed tomography scan low-dose optimization, shows examples described in the literature, briefly discusses new methods for computed tomography scan image processing, and provides conclusions. We propose a pipeline for low-dose computed tomography scan image reconstruction based on the literature. Our proposed pipeline relies on deep learning and big data technology using Spark Framework. We will discuss with the pipeline proposed in the literature to finally derive the efficiency and importance of our pipeline. A big data architecture using computed tomography images for low-dose optimization is proposed. The proposed architecture relies on deep learning and allows us to develop effective and appropriate methods to process dose optimization with computed tomography scan images. The real realization of the image denoising pipeline shows us that we can reduce the radiation dose and use the pipeline we recommend to improve the quality of the captured image

Prevalence of Diabetes and Associated Risk Factors among a Group of Prisoners in the Yaoundé Central Prison

Background. Diabetes is a public health problem worldwide, associated with increased morbidity and mortality. According to the International Diabetes Federation (IDF) 2017 data, around 425 million people worldwide suffer from diabetes. This number is expected to increase to 629 million in 2045. Various occidental studies reported the increased prevalence and lower control of diabetes among prisoners. However, there is no data on the characteristics of inmates with diabetes in sub-Saharan Africa. Methods. A cross-sectional study among incarcerated detainees from the Yaoundé Central Prison was conducted from January to July 2017. Diabetes was defined according to the American Diabetes Association (ADA) criteria. Analyzed variables included phenotypic characteristics, lifestyle, the reason for detention, the sentence severity, and the length of detention. Results. We recruited 437 inmates (344 men) with an average age of 37.0 (95% CI: 35.9-38.3) years. The most frequent age group was 20 to 39 years with 281 (64.7%) inmates, and the mean prison stay was 29.1 (95% CI: 25.7-32.8) months. The prevalence of diabetes in the Yaoundé Central Prison was 9.4%. The main cardiovascular risk factors were a sedentary lifestyle (91.1%), hypertension (39.6%), smoking (31.6%), and alcohol consumption (28.1%). Hypertension (p=0.005), obesity (p=0.0006), smoking (p=0.04), sedentary lifestyle (p=0.04), major crime (p=0.007), and minor crime (p=0.003) were associated with diabetes in univariate analysis. In multivariate analysis, only obesity and sedentary lifestyle were associated with diabetes. Conclusion. Diabetes prevalence in the Yaoundé Central Prison was high, at 9.4%, compared to that in the general population. It was associated with other classical cardiovascular risk factors and factors linked to the sentence (minor and major crimes). This trial is registered with CE00617/CRERSHC/2016