- Book Chapter
24
- 10.1007/978-981-19-6379-7_6
Optimized Nature-Inspired Computing Algorithms for Lung Disorder Detection
- Nov 01, 2022
- V Lakshman Narayana + 3 more +3
Using computational methods inspired by nature, new models for analyzing natural phenomena and behavior can be developed in order to address complex issues. Swarm intelligence and evolutionary computation are just two of the many cutting-edge research areas that fall under this authority. Medical image segmentation has been a major research topic and a major goal in computer vision for a long time. Emerging academic research in biomedical engineering is aided by nature-inspired intelligent methods in solving biomedical engineering problems. To keep up with the most recent developments in biomedical technology, this research includes extensive coverage of relevant topics like machine learning, clinical decision support systems, and swarm intelligence. Medical imaging relies heavily on lung tumor research nowadays because of the impact of COVID-19 and the wealth of morphological and physiological information it provides, which makes diagnosis and treatment planning much simpler. There are numerous detection and segmentation methods in use today, but they all fall short in terms of accuracy. The lung segmentation categorization is based on the usage of a learning algorithm in the selected technique, which is called a filter, a wrapper, or an embedded feature selection method. This research presents the analysis of different natural-inspired computational models in performing segmentation of lung images for the detection of COVID-19 and other health issues. The best performance exhibiting classifier is suggested in the detection process for accurate diagnosis.KeywordsLung disordersNature-inspired algorithmsImage segmentationClassificationCOVID-19Disorder prediction
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