Intelligent Decision Support System for COVID-19 Empowered with Deep Learning

Abstract

The prompt spread of Coronavirus (COVID-19) subsequently adorns a big threat to the people around the globe. The evolving and the perpetually diagnosis of coronavirus has become a critical challenge for the healthcare sector. Drastically increase of COVID-19 has rendered the necessity to detect the people who are more likely to get infected. Lately, the testing kits for COVID-19 are not available to deal it with required proficiency, along with-it countries have been widely hit by the COVID-19 disruption. To keep in view the need of hour asks for an automatic diagnosis system for early detection of COVID-19. It would be a feather in the cap if the early diagnosis of COVID-19 could reveal that how it has been affecting the masses immensely. According to the apparent clinical research, it has unleashed that most of the COVID-19 cases are more likely to fall for a lung infection. The abrupt changes do require a solution so the technology is out there to pace up, Chest X-ray and Computer tomography (CT) scan images could significantly identify the preliminaries of COVID-19 like lungs infection. CT scan and X-ray images could flourish the cause of detecting at an early stage and it has proved to be helpful to radiologists and the medical practitioners. The unbearable circumstances compel us to flatten the curve of the sufferers so a need to develop is obvious, a quick and highly responsive automatic system based on Artificial Intelligence (AI) is always there to aid against the masses to be prone to COVID-19. The proposed Intelligent decision support system for COVID-19 empowered with deep learning (ID2S-COVID19-DL) study suggests Deep learning (DL) based Convolutional neural network (CNN) approaches for effective and accurate detection to the maximum extent it could be, detection of coronavirus is assisted by using X-ray and CT-scan images. The primary experimental results here have depicted the maximum accuracy for training and is around 98.11 percent and for validation it comes out to be approximately 95.5 percent while statistical parameters like sensitivity and specificity for training is 98.03 percent and 98.20 percent respectively, and for validation 94.38 percent and 97.06 percent respectively. The suggested Deep Learning-based CNN model

Treatment Response Prediction in Hepatitis C Patients using Machine Learning Techniques

“…Sentiment/Polarity analysis [19][20][21][22][23][24][25], Rainfall/Weather Prediction [26][27], and Network Intrusion Detection/Network Security [28][29], Software Defect Prediction [30][31][32][33][34][35][36][37][38], Medical and Health data mining [39][40][41][42][43][44][45][46][47]. Machine learning techniques included in this study for the prediction of treatment…”

Study of Different Deep Learning Methods for Coronavirus (COVID-19) Pandemic: Taxonomy, Survey and Insights

“…The experimental results showed that the system obtained an accuracy of 93%, sensitivity of 93%, specificity of 92%, F1 score of 92%, IoU of 85%, and AUC of 93%. In [60], an intelligent decision support system for COVID-19 powered by deep learning (ID2S-COVID19-DL) using X-ray and CT-scan images was presented. The dataset was collected from different sources, such as cameras, X-rays, and CT-scan machines through the Internet of Medical Things (IoMT).…”

COVID-19 Detection from CBC using Machine Learning Techniques

“…Machine learning algorithms are being focused by the many researchers to recognize the hidden patterns as well as to mine the valuable information from raw data. Some of the research fields in which machine learning played a vital role, include: sentiment analysis [12][13][14][15][16][17][18], rainfall prediction [19][20], and network intrusion detection [21][22], software defect prediction [23][24][25][26][27][28][29][30][31][32], health and medical data mining [33][34][35][36][37][38][39][40]. Moreover, a lot of researchers have focused on the use of machine learning techniques to detect covid-19 patients by exploring the patterns in CBC test results, some of the related studies are discussed here.…”