Adaptive UNet-based Lung Segmentation and Ensemble Learning with CNN-based Deep Features for Automated COVID-19 Diagnosis

• Published in: Multimedia tools and applications Vol. 81; no. 4; pp. 5407 - 5441
• Main Author: Das, Anupam
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2022; Springer Nature B.V
• Subjects: Adaptive algorithms; Algorithms; Artificial neural networks; Chest; Classifiers; Computer Communication Networks; Computer Science; Coronaviruses; COVID-19; Data Structures and Information Theory; Deep learning; Developing countries; Disease transmission; Ensemble learning; Feature extraction; Image filters; Image segmentation; Infections; LDCs; Machine learning; Medical imaging; Multimedia Information Systems; Recognition; Special Purpose and Application-Based Systems; Support vector machines; Viral diseases; X-rays; Convolutional Neural Network; Naive Bayes; COVID-19 Detection; Ensemble Learning with CNN-based Deep Features; Autoencoder; Self Adaptive- Tunicate Swarm Algorithm; Adaptive Activation Function-based U-Net; Support Vector Machine

COVID-19 disease is a major health calamity in twentieth century, in which the infection is spreading at the global level. Developing countries like Bangladesh, India, and others are still facing a delay in recognizing COVID-19 cases. Hence, there is a need for immediate recognition with perfect ide...