ABSTRACT
The year 2020 and 2021 was the witness of Covid 19 and it was the leading cause of death throughout the world during this time period. It has an impact on a large geographic area, particularly in countries with a large population. Due to the fact that this novel coronavirus has been detected in all countries around the world, the World Health Organization (WHO) has declared Covid-19 to be a pandemic. This novel coronavirus spread quickly from person to person through the saliva droplets and direct or indirect contact with an infected person. The tests carried out to detect the Covid-19 are time-consuming and the primary cause of rapid growth in Covid19 cases. Early detection of Covid patient can play a significant role in controlling the Covid chain by isolation the patient and proper treatment at the right time. Recent research on Covid-19 claim that Chest CT and X-ray images can be used as the preliminary screening for Covid-19 detection. This paper suggested an Artificial Intelligence (AI) based approach for detecting Covid-19 by using X-ray and CT scan images. Due to the availability of the small Covid dataset, we are using a pre-trained model. In this paper, four pre-trained models named VGGNet-19, ResNet50, InceptionResNetV2 and MobileNet are trained to classify the X-ray images into the Covid and Normal classes. A model is tuned in such a way that a smaller percentage of Covid cases will be classified as Normal cases by employing normalization and regularization techniques. The updated binary cross entropy loss (BCEL) function imposes a large penalty for classifying any Covid class to Normal class. The experimental results reveal that the proposed InceptionResNetV2 model outperforms the other pre-trained model with training, validation and test accuracy of 99.2%, 98% and 97% respectively.
ABSTRACT
The studies claim that COVID-19 has positive impacts on the environment because it minimizes air pollution, water pollution, and noise pollution due to lockdown. On the contrary, COVID-19 is harming the environment due to increased medical wastage. COVID-19 has been declared a pandemic by the World Health Organization (WHO). Due to the exponential growth of COVID-19 cases people using large quantities of medical accessories to shield themselves from coronavirus, a large amount of medical wastage is produced per day. This medical wastage is a major concern for the expert because this medical waste is not adequately handled. The early detection of the COVID patient is the only solution to control this coronavirus. Several COVID detection models have been proposed in the last few months. Most of the existing models have a high false-positive rate where COVID patients are classified as healthy. To address this problem, this paper explores the positive and negative environmental consequences of COVID-19 and suggests a novel method based on artificial intelligence (AI) to identify COVID-19 disease. A comparative analysis of different previously trained models such as Visual Geometry Group Network (VGGNet-19), Residual Network (ResNet50), and Inception ResNet V2 is presented in this paper. Experimental results show that Inception_ResNet_V2 is a better choice for COVID detection. It has a minimal false-positive rate and offers 99.26% and 94% higher training and test accuracy compared to VGGNet and ResNet, respectively. [ABSTRACT FROM AUTHOR] Copyright of Energy Sources Part A: Recovery, Utilization & Environmental Effects is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)