42 research outputs found

    Deep Learning Based Method for Computer Aided Diagnosis of Diabetic Retinopathy

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    © 2019 IEEE. Diabetic retinopathy (DR) is a retinal disease caused by the high blood sugar levels that may damage and block the blood vessels feeding the retina. In the early stages of DR, the disease is asymptomatic; however, as the disease advances, a possible sudden loss of vision and blindness may occur. Therefore, an early diagnosis and staging of the disease is required to possibly slow down the progression of the disease and improve control of the symptoms. In response to the previous challenge, we introduce a computer aided diagnosis tool based on convolutional neural networks (CNN) to classify fundus images into one of the five stages of DR. The proposed CNN consists of a preprocessing stage, five stage convolutional, rectified linear and pooling layers followed by three fully connected layers. Transfer learning was adopted to minimize overfitting by training the model on a larger dataset of 3.2 million images (i.e. ImageNet) prior to the use of the model on the APTOS 2019 Kaggle DR dataset. The proposed approach has achieved a testing accuracy of 77% and a quadratic weighted kappa score of 78%, offering a promising solution for a successful early diagnose and staging of DR in an automated fashion

    Global, regional, and national burden of neurological disorders, 1990–2016 : a systematic analysis for the Global Burden of Disease Study 2016

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    Background: Neurological disorders are increasingly recognised as major causes of death and disability worldwide. The aim of this analysis from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 is to provide the most comprehensive and up-to-date estimates of the global, regional, and national burden from neurological disorders. Methods: We estimated prevalence, incidence, deaths, and disability-adjusted life-years (DALYs; the sum of years of life lost [YLLs] and years lived with disability [YLDs]) by age and sex for 15 neurological disorder categories (tetanus, meningitis, encephalitis, stroke, brain and other CNS cancers, traumatic brain injury, spinal cord injury, Alzheimer's disease and other dementias, Parkinson's disease, multiple sclerosis, motor neuron diseases, idiopathic epilepsy, migraine, tension-type headache, and a residual category for other less common neurological disorders) in 195 countries from 1990 to 2016. DisMod-MR 2.1, a Bayesian meta-regression tool, was the main method of estimation of prevalence and incidence, and the Cause of Death Ensemble model (CODEm) was used for mortality estimation. We quantified the contribution of 84 risks and combinations of risk to the disease estimates for the 15 neurological disorder categories using the GBD comparative risk assessment approach. Findings: Globally, in 2016, neurological disorders were the leading cause of DALYs (276 million [95% UI 247–308]) and second leading cause of deaths (9·0 million [8·8–9·4]). The absolute number of deaths and DALYs from all neurological disorders combined increased (deaths by 39% [34–44] and DALYs by 15% [9–21]) whereas their age-standardised rates decreased (deaths by 28% [26–30] and DALYs by 27% [24–31]) between 1990 and 2016. The only neurological disorders that had a decrease in rates and absolute numbers of deaths and DALYs were tetanus, meningitis, and encephalitis. The four largest contributors of neurological DALYs were stroke (42·2% [38·6–46·1]), migraine (16·3% [11·7–20·8]), Alzheimer's and other dementias (10·4% [9·0–12·1]), and meningitis (7·9% [6·6–10·4]). For the combined neurological disorders, age-standardised DALY rates were significantly higher in males than in females (male-to-female ratio 1·12 [1·05–1·20]), but migraine, multiple sclerosis, and tension-type headache were more common and caused more burden in females, with male-to-female ratios of less than 0·7. The 84 risks quantified in GBD explain less than 10% of neurological disorder DALY burdens, except stroke, for which 88·8% (86·5–90·9) of DALYs are attributable to risk factors, and to a lesser extent Alzheimer's disease and other dementias (22·3% [11·8–35·1] of DALYs are risk attributable) and idiopathic epilepsy (14·1% [10·8–17·5] of DALYs are risk attributable). Interpretation: Globally, the burden of neurological disorders, as measured by the absolute number of DALYs, continues to increase. As populations are growing and ageing, and the prevalence of major disabling neurological disorders steeply increases with age, governments will face increasing demand for treatment, rehabilitation, and support services for neurological disorders. The scarcity of established modifiable risks for most of the neurological burden demonstrates that new knowledge is required to develop effective prevention and treatment strategies. Funding: Bill & Melinda Gates Foundation

    Ammonia Combustion: Opportunities and Challenges

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    Stability of Partially Premixed Ammonia/Methane Flames in a Concentric Flow Conical Nozzle Burner

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    Ammonia, as a carbon-free fuel, holds promise as a potential alternative to fossil fuels. However, its practical implementation requires a comprehensive understanding of its combustion properties, particularly concerning flame stability. This study investigates the stability and flame appearance of partially premixed ammonia-methane flames in a concentric flow conical nozzle burner. The research explores the effects of varying the ammonia fraction in the inner stream, the outer stream velocity, and the degree of partial premixing (expressed as L/D). The findings indicate that flame stability is enhanced by increasing the outer stream equivalence ratio, while it is reduced by increasing the degree of premixing via higher L/D values. Notably, the case of L/D = 0 demonstrates a virtual improvement in flame stability, attributed to the absence of reaction between the inner mixture and the stable outer methane flame. This study provides valuable insights into the combustion characteristics of ammonia-methane blends under partially premixed conditions

    Auto-ignition and numerical analysis on high-pressure combustion of premixed methane-air mixtures in highly preheated and diluted environment

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    This work investigates both autoignition and combustion characteristics in highly preheated and diluted combustion of a laminar premixed stoichiometric CH4/O2/N2 mixture in a cylindrical combustor operating at elevated pressures. The analysis was carried out for a range of operating parameters, including reactant preheat temperatures of 1100–1500 K, combustor pressures of 1-10 atm, and in a highly diluted mixture, achieved by decreasing the oxygen content in the oxidizer from 21% to 3% on volume basis. Simulations were conducted using the laminar premixed adiabatic PFR (plug flow reactor) model of Ansys Chemkin Pro. Two-dimensional pictorial representation was performed using the finite volume-based CFD code Ansys Fluent 19.2. Finite-rate chemistry with the detailed chemical mechanism GRI Mech 3.0 was used for combustion analysis. Results showed that OH and HCO mole fractions decreased with increasing combustor pressure and N2 dilution (or decreased O2 content), while the mole fractions increased with reactant temperature. It was also found that, by reducing the oxygen content in the mixture, the flame stabilized far away from the combustor inlet. In contrast, an increase in combustor pressure and reactant temperature stabilized the flame toward the combustor inlet. These flame stabilization characteristics at different locations of the combustor are explained in terms of ignition delay time, which were calculated using the closed homogenous reactor (CHR) model available in the Ansys Chemkin Pro package. The flame peak temperature decreased with increased N2 dilution and increased by increasing the reactant temperature. Moreover, the peak temperature varied marginally when the combustor pressure was increased. Finally, a regime diagram was prepared to show the various combustion modes, such as HiTAC, MILD combustion, and the no ignition region as a function of O2 content and reactant temperature for different operating pressures. The CO and NO emission were reduced with an increase in pressure in the MILD combustion region
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