Coronavirus (COVID19) is a highly contagious virus which had already killed thousands of people and infected millions more throughout the world. One of the primary challenges that medical practitioners encounter in th...
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Covid 19 is a disastrous infection that the whole world tackled for 2 years from 2020-to 2021. As this virus was new and doctors had no idea about it, they treated patients to save lives with all their possible experi...
详细信息
Coronavirus (COVID19) is a highly contagious virus which had already killed thousands of people and infected millions more throughout the world. One of the primary challenges that medical practitioners encounter in th...
详细信息
Coronavirus (COVID19) is a highly contagious virus which had already killed thousands of people and infected millions more throughout the world. One of the primary challenges that medical practitioners encounter in the realm of healthcare is correctly diagnosing patients conditions and infections. So far, the gold standard screening method RT-PCR test which has been designed to detect covid-19 which only has a positive rate ranging between 30 precent and 60 percent. As a result, a system that can accurately identify images and diagnose or anticipate diseases is needed. As a result, we set out to swiftly create a compact CNN architecture capable of recognizing COVID-19-infected individuals. Different CNN architectures are suggested in this paper to extract information from X-rays which further classified into Covid-19, pneumonia, or healthy. Here, we have used two datasets from publically available repositories that are Kaggle and Mendeley [1] [2]. To see how the size of datasets affects CNN performance, we train the suggested CNNs with both the original and enhanced datasets where datasets are splitted into ratios of 80:20 and 70:30 and the comparison is shown. Also suggested CNN model is compared with the five state-of-art pre-trained models (VGG-16, ResNet50, InceptionV3, EfficientNetB2, DenseNet121) with the same datasets and splitting ratios. we have also used Some visualization methods through which we can get an exact idea of how CNN functions and the explanation behind the network's decisions. This study suggests a model for classifying COVID-19 patients but makes no claims about medical diagnostic accuracy.
Covid 19 is a disastrous infection that the whole world tackled for 2 years from 2020-to 2021. As this virus was new and doctors had no idea about it, they treated patients to save lives with all their possible experi...
Covid 19 is a disastrous infection that the whole world tackled for 2 years from 2020-to 2021. As this virus was new and doctors had no idea about it, they treated patients to save lives with all their possible experiences. This virus was contagious, and lockdown took place, people faced financial problems due to loss of jobs or businesses getting shut down for months, many people could not get admitted to hospitals and lost their life due to lack of facilities. While the whole world was dealing with these numerous issues, there was one kind of group of people who successfully recovered from Covid19 but, they faced some changes in their health like some inability they didn't have before infecting of Covid19 or worsened health issues that were mild in them pre Covid19 infection. These are called post Covid19 symptoms which are seen in post Covid19 patients, and such patients are termed as “Long Haulers”. Long Covid19 symptoms are different in different people, some face mild symptoms like headache, fatigue to severe symptoms where we see an effect on vital organs. In some cases, the effect on the vital organ is irreversible. The conception behind this study is to rack up the data of post Covid19 patients and perform analysis on such data and upskill a ML model, this trained model then project what disease the post Covid19 patient is prone to by taking all his/her details. This idea will help users to get an idea beforehand and take prophylactic measures by conferring with a respective specialist.
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