RESUMO
The Corona Virus was first started in the Wuhan city, China in December of 2019. It belongs to the Coronaviridae family, which can infect both animals and humans. The diagnosis of coronavirus disease-2019 (COVID-19) is typically detected by Serology, Genetic Real-Time reverse transcription-Polymerase Chain Reaction (RT-PCR), and Antigen testing. These testing methods have limitations like limited sensitivity, high cost, and long turn-around time. It is necessary to develop an automatic detection system for COVID-19 prediction. Chest X-ray is a lower-cost process in comparison to chest Computed tomography (CT). Deep learning is the best fruitful technique of machine learning, which provides useful investigation for learning and screening a large amount of chest X-ray images with COVID-19 and normal. There are many deep learning methods for prediction, but these methods have a few limitations like overfitting, misclassification, and false predictions for poor-quality chest X-rays. In order to overcome these limitations, the novel hybrid model called "Inception V3 with VGG16 (Visual Geometry Group)" is proposed for the prediction of COVID-19 using chest X-rays. It is a combination of two deep learning models, Inception V3 and VGG16 (IV3-VGG). To build the hybrid model, collected 243 images from the COVID-19 Radiography Database. Out of 243 X-rays, 121 are COVID-19 positive and 122 are normal images. The hybrid model is divided into two modules namely pre-processing and the IV3-VGG. In the dataset, some of the images with different sizes and different color intensities are identified and pre-processed. The second module i.e., IV3-VGG consists of four blocks. The first block is considered for VGG-16 and blocks 2 and 3 are considered for Inception V3 networks and final block 4 consists of four layers namely Avg pooling, dropout, fully connected, and Softmax layers. The experimental results show that the IV3-VGG model achieves the highest accuracy of 98% compared to the existing five prominent deep learning models such as Inception V3, VGG16, ResNet50, DenseNet121, and MobileNet.
RESUMO
BACKGROUND: This study presents our data on mortality in end stage renal disease (ESRD) patients on peritoneal dialysis (PD) who developed COVID-19. MATERIALS AND METHODS: Sri Padmavathi Medical College Hospital, Sri Venkateswara Institute of Medical Sciences University, was designated the State COVID Hospital in March 2020. In a retrospective observational study, we collected the data of ESRD patients on PD and identified the risk factors for mortality. RESULTS: Prior to the pandemic, 136 patients with ESRD were on peritoneal dialysis at our Institute. Among them, 27 (19.8%) eventually developed COVID-19, and 14 of them (51.8%) died. Serum albumin levels were lower and D-dimer levels were significantly higher in deceased patients than in survivors. DISCUSSION: The mortality rate in ESRD patients on PD with COVID-19 at our institution was higher than in other published studies.
