Medical Informatics and Bioimaging Using Artificial Intelligence (eBook, PDF)

Challenges, Issues, Innovations and Recent Developments
Redaktion: Hassanien, Aboul Ella; Yasin Shams, Mahmoud; Snásel, Václav; Bhatnagar, Roheet

• Format: PDF

Produktbeschreibung

This book emphasizes the latest developments and achievements in artificial intelligence and related technologies, focusing on the applications of artificial intelligence and medical diagnosis. The book describes the theory, applications, concept visualization, and critical surveys covering most aspects of AI for medical informatics.

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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 258
• Erscheinungstermin: 15. Dezember 2021
• Englisch
• ISBN-13: 9783030911034
• Artikelnr.: 63227737

Inhaltsangabe

Optimized Deep Learning Using Cockroach Swarm Optimization for Segmenting Brain MRI Scans.- Efficient Classification Model for Melanoma Based on Convolutional Neural Networks.- Machine Learning-supported MRI Analysis of Brain Asymmetry for Early Diagnosis of Dementia.- Feature Selection Based Coral Reefs Optimization for Breast Cancer Classification.- A Comprehensive Review on Brain Disease Mapping - the Underlying Technologies and AI based Techniques for Feature Extraction & Classification using EEG signals.- Recognition of Ocular Disease Based Optimized VGG-Net Models.- Applications of AI and IoT in COVID-19 Vaccine and Its Impact on Social Life.- COVID-19 forecasting based on an improved interior search algorithm and multilayer feed-forward neural network.- Development of Disease Diagnosis Model for CXR Images and Reports - A Deep Learning Approach.- Intelligent Drug Descriptors Analysis: Toward COVID-19 Drug Repurposing.- Analysis of Aortic Valve Using a Finite Element Model.- Fundus images Enhancement using Using Gravitational Force and Lateral Inhibition Network for Blood Vessel Detection.- Multivariate Fuzzy Logic based Smart Healthcare Monitoring for Risk Evaluation of Cardiac Patients.- Ensemble machine learning model for mortality prediction inside ICU