Deep Learning Models towards Health Informatics Management Foundations, Challenges and Opportunities

This book provides a comprehensive discussion of deep learning (DL) in medical and healthcare applications, including the fundamentals and current advances in medical image analysis, deep learning methods for medical image analysis, and deep learning-based clinical computer-aided diagnosis systems. It further presents algorithms, models, software, and tools in the field of bioinformatics. This book: Presents mathematical principles of deep learning algorithms such as convolutional neural networks, and recurrent neural networks. Discusses applications of deep learning such as hyperparameter optimization and multimodal deep learning for bioinformatics. Showcases how algorithms are applied to a broad range of application areas, including microscopy and pathology. Covers deep learning techniques such as deep feedforward networks, sequence modeling, and convolutional networks. Examines the importance of deep learning in biomedical image processing and enhancing biological diagnosis. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the areas such as electrical engineering, electronics, and communications engineering, computer engineering, and information technology.

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