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portada Medical Informatics and Bioimaging Using Artificial Intelligence: Challenges, Issues, Innovations and Recent Developments
Type
Physical Book
Publisher
Language
English
Pages
258
Format
Hardcover
Dimensions
23.4 x 15.6 x 1.6 cm
Weight
0.55 kg.
ISBN13
9783030911027

Medical Informatics and Bioimaging Using Artificial Intelligence: Challenges, Issues, Innovations and Recent Developments

Hassanien, Aboul Ella ; Bhatnagar, Roheet ; Snásel, Václav (Author) · Springer · Hardcover

Medical Informatics and Bioimaging Using Artificial Intelligence: Challenges, Issues, Innovations and Recent Developments - Hassanien, Aboul Ella ; Bhatnagar, Roheet ; Snásel, Václav

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Origin: U.S.A. (Import costs included in the price)
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Synopsis "Medical Informatics and Bioimaging Using Artificial Intelligence: Challenges, Issues, Innovations and Recent Developments"

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

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