Ensemble Deep Convolution Neural Network for Sars-Cov-V2 Detection

Abstract

The continuing Covid-19 pandemic, caused by the SARS-CoV2 virus, has attracted the eye of researchers and many studies have focussed on controlling it. Covid-19 has affected the daily life, employment, and health of human beings along with socio-economic disruption. Deep Learning (DL) has shown great potential in various medical applications in the past decade and continues to assist in effective medical image analysis. Therefore, it is effectively being utilized to explore its potential in controlling the pandemic. Chest X-Ray (CXR) images were used in studies pertaining to DL for medical image analysis. With the burgeoning of Covid-19 cases by day, it becomes imperative to effectively screen patients whose CXR images show a tendency of Covid-19 infection. Several innovative Convolutional Neural Network (CNN) models have been proposed so far for classifying medical CXR images. Moreover, some studies used a transfer learning (TL) approach on state-of-art CNN models for the classification task. In this paper, we do a comparative study of these CNN models and TL approaches on state-of-art CNN models and have proposed an ensemble Deep Convolution Neural Network model (DCNN)

An Adaptive Grid Search Based Efficient Ensemble Model for Covid-19 Classification in Chest X-Ray Scans

“…Treatment for COVID-19 patients is challenging, as there is currently no cure, and patients often require hours of waiting time [4]. To address these challenges, chest imaging has emerged as an alternative to RT-PCR, and a small dataset of X-Ray images related to COVID-19 has become helpful for training machine learning (ML) algorithms to detect the virus automatically [5,6]. With the increase in Computer Diagnostic Systems using artificial intelligence systems detecting the presence or absence of diseases has become faster and more efficient.…”