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A Machine Learning-based Method for COVID-19 and Pneumonia Detection
Biomimetic Synthesis of Calcium Carbonate in Bile in the presence of Amino Acids
The Influence of Low Pesticide Doses on Fusarium Molds
Deep Learning-based Multi-class Three-dimensional (3-D) Object Classification using Phase-only Digital Holographic Information
The Influence of Dynamical Downscaling and Boundary Layer Selection on Egypt’s Potential Evapotranspiration using a Calibrated Version of the Hargreaves-samani Equation: RegCM4 Approach
Estimate of Temperature Rise in the 21st Century for Various Scenarios
The Imperative for Modern Public Cloud Providers to Upgrade Their Data Centers
Assessing Bee (Hymenoptera, Apoidea, Anthophila) Diversity and Floral Preference in Two Habitats in the Iberian Peninsula
DNA Genetics and UHPLC-Q-TOF-MS Analysis of Phytochemicals for Asparagus racemosus Roots
Physical Activity and Lifestyle of Female Students of the Faculty of Health Sciences University of Applied Sciences in Tarnów (Poland)
Exploring Markov Decision Processes: A Comprehensive Survey of Optimization Applications and Techniques
Contamination in Heat Exchangers: Types, Energy Effects and Prevention Methods
Lattice Boltzmann Method without Invoking the M << 1 Assumption
A New Physical Phenomenon Discovered When Microbiology Meets Surrealism: The Yoshida Effect has the Power to Fuse Bacteria and Nano-Acicular Materials
Auxological Status of Modern Primary School Students of Nizhny Novgorod Region
Relationship between Sustainable Development, Economy and Poverty
A Study of Multi-Pose Effects On a Face Recognition System
Analysis of Reliable Transmission Performance Optimization Methods for Satellite-to-Ground Laser Communication Links
The Effect of Stacking Sequence and Ply Orientation with Central Hole on Tensile Behavior of Glass Fiber-polyester Composite
From Motor Augmentation to Body Enhancement, the Harmony between Human Body and Artificial Parts
Mars Ascent Propellants and Life Support Resources - Take it or Make it?
The Power of Artificial Intelligence for Improved Patient Outcomes, Ethical Practices and Overcoming Challenges
Country Risk to Face Global Emergencies: Negative Effects of High Public Debt on Health Expenditures and Fatality Rate in COVID-19 Pandemic Crisis
Levetiracetam-induced Rhabdomyolysis - A Rare Complication
Machine Learning Applied to Electric Vehicle Routing Problem: Optimizing Costs for a Sustainable Environment
Harnessing Microbes for the Benefit of Man
Research Article
A Machine Learning-based Method for COVID-19 and Pneumonia Detection
Pneumonia is described as an acute infection of lung tissue produced by one or more bacteria, and Coronavirus Disease (COVID-19) is a deadly virus that affects the lungs of the human body. The symptoms of COVID-19 disease are closely related to pneumonia. In this work, we identify the patients of pneumonia and coronavirus from chest X-ray images. We used a convolutional neural network for spatial feature learning from X-ray images. We experimented with pneumonia and coronavirus X-ray images in the Kaggle dataset. Pneumonia and corona patients are classified using a feed-forward neural network and hybrid models (CNN+SVM, CNN+RF, and CNN+Xgboost). The experimental findings on the Pneumonia dataset demonstrate that CNN detects Pneumonia patients with 99.47% recall. The overall experiments on COVID-19 x-ray images show that CNN detected the COVID-19 and pneumonia with 95.45% accuracy.
