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    Handbook of Deep Learning Models for Healthcare Data Processing: Disease Prediction, Analysis, and Applications

    Handbook of Deep Learning Models for Healthcare Data Processing by Kumar, Ajay; Dembla, Deepak; Tinker, Seema;

    Disease Prediction, Analysis, and Applications

    Sorozatcím: Advancements in Intelligent and Sustainable Technologies and Systems;

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        75 915 Ft (72 300 Ft + 5% áfa)
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      • Discounted price 60 732 Ft (57 840 Ft + 5% áfa)

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    Rövid leírás:

    In recent years, deep learning has shown great potential in transforming various fields, including healthcare. With the abundance of healthcare data being generated daily, there is a pressing need to develop efficient algorithms that can process and analyze this data to improve patient care and treatment outcomes.

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    Hosszú leírás:

    In recent years, deep learning has shown great potential in transforming various fields including healthcare. With the abundance of healthcare data being generated every day, there is a pressing need to develop efficient algorithms that can process and analyze this data to improve patient care and treatment outcomes.


    Handbook of Deep Learning Models for Healthcare Data Processing: Disease Prediction, Analysis, and Applications covers a wide range of deep learning models, techniques, and applications in healthcare data processing, analysis, and disease prediction, providing a comprehensive overview of the field. It focuses on the practical application of deep learning models in healthcare and offers step-by-step instructions for building and deploying models and using real-world examples. The handbook discusses the potential future applications of deep learning models in healthcare, such as precision medicine, personalized treatment, and clinical decision support. It also addresses the ethical considerations associated with the use of deep learning models in healthcare, such as privacy, security, and bias. It provides technical details on deep learning models, including their architecture, training methods, and optimization techniques, making it useful for data scientists and researchers.


    Written to be a comprehensive guide for healthcare professionals, researchers, and data analysts, this handbook is an essential need for those who are interested in using deep learning models to analyze and process healthcare data. It is also suitable for those who have a basic understanding of machine learning and want to learn more about the latest advancements in deep learning in healthcare.

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    Tartalomjegyzék:

    Section 1: Emerging Technologies of Deep Learning in Healthcare. 1. Deep Learning Models for Electronic Health Record (EHR) Data Analysis. 2. An Extensive Study of Disease Prediction Models using Machine Learning. 3. Deep Learning Approaches for Alzheimer's disease Diagnosis: A Comparative Study of ResNet50, CNN, and MobileNet. 4. Sentiment Classification Analysis Using Deep Learning Network Models. 5. Predictive Modeling of Herbal-Drug Interactions using Mathematical Approaches. 6. Revolutionizing Breast Cancer Detection: A Shallow Neural Network Approach for Accurate Classification of Calcifications and Masses in Mammographic Scans. 7. Artificial Intelligence-Based Automated Detection of Rheumatoid Arthritis: A Review. 8. Medical Imaging Analysis Techniques: Advances, Challenges, and Future Directions. 9. Modeling the Transtheoretical Model for Health Behavior Stage Analysis: Tool Development and Testing. Section 2: Deep Learning Analytics in Healthcare. 10. Utilization of OCR and LLM to decode medical diagnostics/prescriptions into general-purpose language. 11. A state-of-the-art model for drug classification using image recognition. 12. Transforming Healthcare with Blockchain-based Smart Contracts: A Focus on Quality-of-Service. 13. Prototype Model for Face and Skin-Related Disease Detection Using Deep Learning and Image Recognition. 14. Brain Computer Interface (BCI)-Inspired Arduino Based Robotic Brain Controller. 15. Transfer Learning-based Framework for Human Skin Cancer Evaluation . 16. Healthcare Reimagined: AI's Impact on Diagnosis and Treatment. 17. Advanced LSTM Approach for Aspect-based Sentiment Classification. 18. A Review on Patch-based Medical Image Classification using Convolutional Neural Network (CNN).

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    Handbook of Deep Learning Models for Healthcare Data Processing: Disease Prediction, Analysis, and Applications

    Handbook of Deep Learning Models for Healthcare Data Processing: Disease Prediction, Analysis, and Applications

    Kumar, Ajay; Dembla, Deepak; Tinker, Seema;(ed.)

    75 915 Ft

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