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, DL methods for medical image analysis, and DL-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 DL algorithms such as convolutional neural networks and recurrent neural networks
• Discusses applications of DL such as hyperparameter optimization and multimodal DL for bioinformatics
• Showcases how algorithms are applied to a broad range of application areas, including microscopy and pathology
• Covers DL 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 undergraduates, graduate students, and academic researchers in the areas such as electrical engineering, electronics, and communications engineering, computer engineering, and information technology.

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, DL methods for medical image analysis, and DL-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 DL algorithms such as convolutional neural networks and recurrent neural networks
• Discusses applications of DL such as hyperparameter optimization and multimodal DL for bioinformatics
• Showcases how algorithms are applied to a broad range of application areas, including microscopy and pathology
• Covers DL 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 undergraduates, graduate students, and academic researchers in the areas such as electrical engineering, electronics, and communications engineering, computer engineering, and information technology.

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, DL methods for medical image analysis, and DL-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 DL algorithms such as convolutional neural networks and recurrent neural networks
• Discusses applications of DL such as hyperparameter optimization and multimodal DL for bioinformatics
• Showcases how algorithms are applied to a broad range of application areas, including microscopy and pathology
• Covers DL 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 undergraduates, graduate students, and academic researchers in the areas such as electrical engineering, electronics, and communications engineering, computer engineering, and information technology.