ABSTRACT
BACKGROUND AND MOTIVATION: The novel coronavirus causing COVID-19 is exceptionally contagious, highly mutative, decimating human health and life, as well as the global economy, by consistent evolution of new pernicious variants and outbreaks. The reverse transcriptase polymerase chain reaction currently used for diagnosis has major limitations. Furthermore, the multiclass lung classification X-ray systems having viral, bacterial, and tubercular classes-including COVID-19-are not reliable. Thus, there is a need for a robust, fast, cost-effective, and easily available diagnostic method. METHOD: Artificial intelligence (AI) has been shown to revolutionize all walks of life, particularly medical imaging. This study proposes a deep learning AI-based automatic multiclass detection and classification of pneumonia from chest X-ray images that are readily available and highly cost-effective. The study has designed and applied seven highly efficient pre-trained convolutional neural networks-namely, VGG16, VGG19, DenseNet201, Xception, InceptionV3, NasnetMobile, and ResNet152-for classification of up to five classes of pneumonia. RESULTS: The database consisted of 18,603 scans with two, three, and five classes. The best results were using DenseNet201, VGG16, and VGG16, respectively having accuracies of 99.84%, 96.7%, 92.67%; sensitivity of 99.84%, 96.63%, 92.70%; specificity of 99.84, 96.63%, 92.41%; and AUC of 1.0, 0.97, 0.92 (p < 0.0001 for all), respectively. Our system outperformed existing methods by 1.2% for the five-class model. The online system takes <1 s while demonstrating reliability and stability. CONCLUSIONS: Deep learning AI is a powerful paradigm for multiclass pneumonia classification.
ABSTRACT
BACKGROUND: In the wake of the Covid-19 Pandemic, parts of the public health system at increased risk of reduced efficiency include healthcare services for women and children. This in turn could reverse all the progress achieved over the years in reducing maternal and child mortality. In this study, an attempt has been made to assess the indirect effect of the pandemic on maternal and child health services in public health facilities. METHODS: Data pertaining to maternal and child health services being provided under specific Government programmes, were collected from public health facilities of District Sant Kabir Nagar in Uttar Pradesh, India. Comparative analysis of the data from the pandemic phase with data from the year 2019 was done to determine the impact on services. RESULTS: Reduced coverage across all maternal and child health interventions was observed in the study. There was an overall decrease of 2.26 % in number of institutional deliveries. Antenatal care services were the worst affected with 22.91% decline. Immunization services were also dramatically decreased by more than 20%. CONCLUSION: The response of the public healthcare delivery system to the Covid-19 Pandemic is negatively affecting both the provision and utilization of maternal and child healthcare services. It is deterrent to the progress achieved in maternal and child health parameters over the years. Better response strategies should be put in place to minimize lag in service deliwvery.