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.

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.