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Full Description
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.
Contents
1. Healthcare Informatics for Analyzing Patient Health Records. 2. Advancements in Deep Learning for Medical Image Representation Techniques. 3. Secure Steganographic Medical Image Compression by Using Winged Herbi-Hopper Optimization Algorithm. 4. Shedding Light into the Dark: Early Oral Cancer Detection using Hyperspectral Imaging. 5. Seeing the Unseen: An Automated Early Breast Cancer Detection Using Hyperspectral Imaging. 6. Deep Generative Models and Challenges in Synthesizing Histopathological Images for Breast Cancer Diagnosis. 7. Deep Learning-Based Approach for Automated Cataract Detection. 8. Analysis of Vision Health Assessment and Diagnosis Using Advanced Deep Learning Techniques. 9. Diabetic Retinopathy Detection Using Fine-Tuned ResNet-50, ResNet-152, and a Hybrid Classical-Quantum Model: A Comprehensive Deep Learning Approach. 10. Deep Learning for Automated Tumor Segmentation in MRI Images. 11. Neural Models for Embodied AI Agents in Healthcare: Enhancing Patient Interaction, Diagnosis, and Treatment through Autonomous Learning Systems. 12. Embodied AI in Healthcare and Assistive Robotics. 13. Smart Human Intrusion Prevention: YOLO and CNN-Based Detection and Alerting System. 14. Deep Learning for Clinical Decision Support Systems.
