An approach to categorize chest X-ray images using sparse categorical cross entropy

• DOI: 10.11591/ijeecs.v24.i3.pp1700-1710
• Publication Date: 2021-12-09T03:06:16Z
• Authors: Chaithanya B. N.; Swasthika Jain T. J.; A. Usha Ruby; Ayesha Parveen
• Keywords: Piezoelectric energy; Face detection; Big data analytics; Traffic collision; Metamaterial; Virtual learning; Stochastic gradient descent; Mathematical model; Photovoltaic-thermal system; Percentage; Long baseline; Auxiliary winding voltage; Wireless power transfer; VHDL approach; Agriculture 4.0; Hafnium oxide; Dynamic power optimization; Data correction; Ackermann steering; Membership function; Vocal music; EDCA; Instagram; Customers; Hot electron; Outage probability; Close; MATLAB; Unsupervised classification; Non-chemical energy storage; Sputter-deposited; Grade point average; Digital filter design; Broken bar; Conditional random fields; Big data; Restoring division algorithm; Machine learning; Digital modulation; Equalization; Rimigs; PI controller; Distance learning; Resistive switching; Deep learning; Full-duplex DF relaying; BGSLibrary; Social CRM; Monitoring system; Gene expression; Cold start; Kinetic energy; Power oscillations; Dataset; Lissajous curve; Low power division algorithm; Logistic regression; Sparse categorical crossentropy; Genetic mutation; Electric vehicle; Photovoltaic energy; Multi-layer MTM superstrate; Centroid-based clustering; Digital agriculture; Diagnosis; Decision tree; 0202 electrical engineering, electronic engineering, information engineering; UPFC; Integrated converter; Moving detection; Video monitoring; 2. Zero hunger; Authentication; Particle swarm optimization; Arduino Uno; 05 social sciences; Mobility management; Actor-critic framework; Deep Q-networks; Gamification; https://purl.org/pe-repo/ocde/ford#5.03.01; Dilation; Physical unclonable function; Preemption; Motivation and performance; Foreground; MATLAB GUI; Powered wheelchair; NodeMCU ESP8266; Bayesian point estimation; Optimization; Request available; Virtual education; KNN; Swarm intelligence; Wait-for-graph; Image processing; Octalysis framework; Morphological operation; FACTS; Log-normal fading; 14. Life underwater; Bipolar switching; Silicon oxide; Perpendicular plane; Field measurement; HOMER; Audio classification; COVID-19; TCP/IP networks; Tongue gesture; Maximum power point tracking; Automated stock trading; Prediction; Superstrate; Markov decision process; XGBoost; Energy storage; Gravitating disk; Facebook; Support vector machine; Cumulative grade point average; Thermoelectric energy; Banana leaf diseases; 02 engineering and technology; Data clustering; 01 natural sciences; 7. Clean energy; Medoid-based clustering; Time-of-arrival; Color feature extraction; Microstrip antenna; Surveillance system; Deep reinforcement learning; Energy harvesting; 3. Good health; Hybrid TS-PS; Erosion; Hybrid artificial neural network; Switchable resonator; Human machine interface; Acoustic signal processing; Turbine; Differential equations; IoT; Non-restoring division algorithm; k-Nearest neighbors; Bioinformatics; Open; Music information retrieval; Induction motor drive; QoS; Recommendation system; Redundancy; Active cooling; Multilayer perceptron; Motion of a material point; Pneumonia viral; Confidence interval; Unipolar switching; IEEE-802.11ac; Prophet algorithm; Fuzzy logic; Localization; Teacher performance; Cosine similarity; Log-loss; 0503 education; Tifinagh; Student perception; Multimedia services; Handover; CFD simulator; Vehicle; Smart farming; Consecutive k-out-of-n:F system; Game elements; Mouth gesture; Multi-class classification; Flow optimization; 11. Sustainability; Trend; Single phase charging; Evaluation; Amazigh language; Chemical energy storage; 4. Education; SIW resonator; Substrate integrated waveguide; Renewable hybrid system backtrack search algorithm; Temperature; Deadlock; OMNet++ modeler v5.5.1; Prediction accuracy; Classification; Frequency regulation; Part of speech; Dynamic power reduction techniques; SPDT switch; Problem; Steady state availability; SVM; Filter window; Heterogeneous network; Vibration; Squirrel cage motor; Machine learning model; Lung cancer disease; 0103 physical sciences; Personalized cancer treatment; Autonomous underwater vehicles; Sensors; Green energy; Millimeter wave; UE speed; ANN controller; Mathematical morphology; 13. Climate action; Background subtraction; Potential; Village tourism; Wireless sensor network

The Coronavirus disease (COVID-19) pandemic is the most recent threat to global health. Reverse transcription-polymerase chain reaction (RT-PCR) testing, computed tomography (CT) scans, and chest X-ray (CXR) images are being used to identify Coronavirus, one of the most serious community viruses of the twenty-first century. Because CT scans and RT-PCR analyses are not available in most health divisions, CXR images are typically the most time-saving and cost-effective tool for physicians in making decisions. Artificial intelligence and machine learning have become increasingly popular because of recent technical advancements. The goal of this project is to combine machine learning, deep learning, and the health-care sector to create a categorization technique for detecting the Coronavirus and other respiratory disorders. The three conditions evaluated in this study were COVID-19, viral Pneumonia, and normal lungs. Using X-ray pictures, this research developed a sparse categorical cross-entropy technique for recognizing all three categories. The proposed model had a training accuracy of 91% and a training loss of 0.63, as well as a validation accuracy of 81% and a validation loss of 0.7108.