CIDC-Net: Chest-X Ray Image based Disease Classification Network using Deep Learning

Recently, COVID-19 is spreading rapidly and fast detection of COVID-19 can save millions of lives. Further, the COVID-19 can be detected easily from chest x ray (CXR) images using artificial intelligence methods. However, the performance of these application and methods are reduced due to noises presented in the CXR images, which degrading the performance of overall systems. Therefore, this article is focused on implementation of an innovative method for quickly processing CXR images of low quality, which enhances the contrast using fuzzy logic. This method makes use of tuned fuzzy intensification operators and is intended to speed up the processing time. Therefore, this work is focused on implementation of CXR image-based disease classification network (CIDC-Net) for identification of COVID-19 and pneumonia related 21 diseases. The CIDC-Net utilizes the deep learning convolutional neural network (CNN) model for training and testing. Finally, the simulations revealed that the proposed CIDC-Net resulted in superior performance as compared to existing models. © 2022 IEEE.

The COVID-19 pandemic's impact on all-cause mortality disparities in Medicare: By race, income, chronic health, mental/behavioral health, disability.

BACKGROUND: The Medicare-enrolled population is heterogeneous across race, ethnicity, age, dual eligibility, and a breadth of chronic health, mental and behavioral health, and disability-related conditions, which may be differentially impacted by the COVID-19 pandemic. OBJECTIVE: To quantify changes in all-cause mortality prior-to and in the first year of the COVID-19 pandemic across Medicare's different sociodemographic and health-condition subpopulations. METHODS: This observational, population-based study used stratified bivariate regression to investigate Medicare fee-for-service subpopulation differences in pre-pandemic (i.e., 2019 versus 2016) and pandemic-related (2020 versus 2019) changes in all-cause mortality. RESULTS: All-cause mortality in the combined Medicare-Advantage (i.e., managed care) and fee-for-service beneficiary population improved by a relative 1% in the ten years that preceded the COVID-19 pandemic, but then escalated by a relative 15.9% in 2020, the pandemic's first year. However, a closer look at Medicare's fee-for-service subpopulations reveals critical differences. All-cause mortality had actually been worsening prior to the pandemic among most psychiatric and disability-related condition groups, all race and ethnicity groups except White Non-Hispanic, and Medicare-Medicaid dual-eligible (i.e., low-income) beneficiaries. Many of these groups then experienced all-cause mortality spikes in 2020 that were over twice that of the overall Medicare fee-for-service population. Of all 61 chronic health conditions studied, beneficiaries with schizophrenia were the most adversely affected, with all-cause mortality increasing 38.4% between 2019 and 2020. CONCLUSION: This analysis reveals subpopulation differences in all-cause mortality trends, both prior to and in year-one of the COVID-19 pandemic, indicating that the events of 2020 exacerbated preexisting health-related inequities.

Covid Complications: An Overview

Coronavirus disease 19 (COVID-19) is taken into consideration as a multisystemic disease. Several studies have reported persistent symptoms or late-onset complications after acute COVID-19, including Pulmonary, Immunological conditions, Hepatic and hematological disorders. Many people in India encountered financial hardship as a result of the COVID 19 pandemic, thus the government waived cost-sharing for COVID 19 testing and treatment. Patients are still in danger for lung disease, cardiovascular disease, and psychological state problems even after they have recovered. Diabetes is a common comorbidity with Corona virus infection, and it plays a significant impact in the severity of Covid 19 infection. Various Adverse events that developed over the course of COVID-19 and Patients are facing many long-term consequences due to its treatment. Patients with lower incomes, those that are uninsured or underinsured, are likely to face significant medical, psychological, and financial problems as results of these issues patients are enduring delayed morbidity and impairment.