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Artificial Intelligence for Innovative Healthcare Informatics / edited by Shabir Ahmad Parah, Mamoon Rashid, Vijayakumar Varadarajan.
- Format:
- Book
- Series:
- Computer Science (SpringerNature-11645)
- Language:
- English
- Subjects (All):
- Medical informatics.
- Artificial intelligence.
- Image processing.
- Machine learning.
- Health Informatics.
- Artificial Intelligence.
- Image Processing.
- Machine Learning.
- Local Subjects:
- Health Informatics.
- Artificial Intelligence.
- Image Processing.
- Machine Learning.
- Physical Description:
- 1 online resource (VI, 327 pages) : 94 illustrations, 79 illustrations in color.
- Edition:
- 1st ed. 2022.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2022.
- System Details:
- text file PDF
- Summary:
- There are several popular books published in Healthcare Computational Informatics like Computational Bioengineering and Bioinformatics (2020), Springer; Health Informatics (2017), Springer; Health Informatics Vision: From Data via Information to Knowledge (2019), IOS Press; Data Analytics in Biomedical Engineering and Healthcare (2020), Elsevier. However, in all these mentioned books, the challenges in Biomedical Imaging are solved in one dimension by use of any specific technology like Image Processing, Machine Learning or Computer Aided Systems. In this book, the book it has been attempted to bring all technologies related to computational analytics together and apply them on Biomedical Imaging.
- Contents:
- Section 1: Medical Image Analysis using Artificial Intelligence
- Use of Deep Learning in Biomedical Imaging.-Detection of Breast Cancer Masses in Mammogram Images with Watershed Segmentation and Machine Learning Approach
- Cloud-based Glaucoma Diagnosis in Medical Imaging using Machine Learning
- Leucocytic Cell Nucleus Identification using Boundary Cell Detection algorithm with Dilation and Erosion based Morphometry
- Effective Prediction of Autism Using Ensemble Method.-Section 2: Artificial Intelligence (AI) Classification Models for COVID-19 Pandemic.-Automatic Classification of COVID-19 infected patients using Convolution Neural Network Models.-AI-Based Deep Random Forest Ensemble Model for Prediction of COVID-19 and Pneumonia from Chest X-Ray Images
- Section 3: Use of AI-Enabled IoT in Healthcare
- Internet of Things and Artificial Intelligence in Biomedical Systems.-Role of IoT in Healthcare Sector for Monitoring Diabetic Patients
- Section 4: Applications of Artificial Intelligence in Healthcare
- Low-Rank Representation based approach for subspace segmentation and clustering of biomedical image patterns.-Performance Comparison of Imputation Methods for Heart Disease Prediction
- Ayurnano: A solution towards herbal therapeutics using Artificial Intelligence approach
- Artificial Intelligence in Biomedical Education
- The Emergence of Natural Language Processing (NLP) Techniques in Healthcare AI
- Prospects and Difficulties of Artificial Intelligence (AI) Implementations in Naturopathy.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-030-96569-3
- 9783030965693
- Access Restriction:
- Restricted for use by site license.
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