A Systematic Literature Review in Diagnosis and Severity Estimation of COVID-19 Disease using Machine Learning

In identification of severe acute respiratory syndrome corona virus 2(SARS-CoV-2), the novel corona virus responsible for COVID-19, professionals related to medical domain have been entered to implement various novel technical solutions and patient diagnosis techniques. The COVID-19 pandemic has accelerated enforcement of machine learning (ML) technology, and various other such organizational groups have been eager to embrace and adjust these ML techniques to the outbreak concerns. We have carried out a tremendous analysis based on the literature available till now. The complete assessment carried related to the use of machine learning models to fight against COVID-19, emphasis on various aspects like disease effects, it's diagnosis, percentage of severity estimation, drug and treatment analysis, effective feature selection, and also post-Covid context related. A systematic search of online research repositories which are Google Scholar, Web of Science and PubMed was undertaken in corresponding to the "Preferred Reporting items for Meta-Analysis and Systematic Reviews" criteria to find all related published papers during 2020 and 2022 years. The search process was created by integrating COVID-19-typical terms with the word "machine learning.". Copyright © 2022, Anka Publishers. All rights reserved.

Prediction of COVID-19 Through Chest X-Ray Images Employing Various Machine Learning Techniques

The COVID-19 is the most infectious disease which is recently discovered. The COVID-19 pandemic has led to excruciating loss to human life and it also caused mild to severe respiratory illness, including death. Detecting the infected patients and taking special care is crucial in fighting covid-19. Radiography and Radiology images are used to diagnose the patients. These are the fastest ways to identify COVID-19 disease. It is observed that patients affected with COVID-19 have specific abnormalities in their chest radiograms. Initially there were few limited set of CT images are available publicly in performing research. Board-certified radiologist role is to perform identification of images exhibiting COVID-19 disease. Chest CT scans are helpful to diagnose COVID-19 disease in individuals. COVID-19 directly shows impact on lungs and it damages and the tiny air sacs. In this paper we have used deep transfer learning models Residual Network (ResNet50) and VGG19 (Visual Geometry Group) to predict the disease at earlier stages. These models obtained a specificity rate of 90% and achieved a highest sensitivity rate of 98 %. In addition to sensitivity and specificity rates ROC curve, average prediction and confusion matrix of each model are presented in the papers. While this achieved performance is very encouraging if we have large set of COVID-19 images then it may give more reliable estimation of accuracy rates. © 2022 IEEE.

Pneumonia_Net Model: Deep Learning Based Intelligent System forPneumonia Classification using Chest X-ray Images

Radiologists are faced with a challenging problem whenever they have to classify the anomalies shown on chest x-rays. Because of this, throughout the course of the last few decades, computer aided diagnostic (CAD) systems have been created to extract meaningful information from X-rays in order to assist medical professionals in gaining a quantitative understanding of an X-ray.Because radiology is such an important field, most of the time the analysis of radiologist images is carried out by trained medical professionals. This is due to the fact that patients seek the highest possible level of treatment in addition to the highest possible quality, regardless of how much it costs.However, its complexity and the subjective nature of the visuals limit its usefulness. There is a great deal of diversity between different translators and a great deal of exhaustion in human professional image processing. Our main goal is to classify lung disorders utilizing diagnostic X-ray images analysed using deep learning and images exploited using Pandas, Keras, Open CV, Tensor Flow, etc. Chest radiographs are still diagnosed by doctors and radiologists using manual and visual methods. As a result, a system capable of diagnosing chest X-rays must be developed that is both smart and automated. The goal of this study is to classify chest X-ray images into normal and pathological using a deep neural network model called Pneumonia Net. It is trained and evaluated using chest X-rays taken from publicly available databases that include both normal and pathological radiographs. Due to their capacity to automatically extract high-level representations from large data sets, CNN-based deep learning categorization approaches outperform existing picture classification methods in this regard. Three different network models are compared depending on their performance. In experiments, it was found that the Pneumonia Net model had a good generalisation capacity in identifying unseen chest X-rays as normal or anomalous, and that its performance was better than that of other network models.