Emerging Trends and Applications of Deep Learning for Biomedical Data Analysis

Emerging Trends and Applications of Deep Learning for Biomedical Data Analysis by Smita Sharma is an authoritative, up-to-date guide that bridges cutting-edge AI research and practical biomedical practice. Ideal for researchers, data scientists, clinicians, and graduate students, this book unlocks how deep learning is transforming medical imaging, genomics, electronic health records (EHR) analysis, drug discovery, and wearable sensor data across global healthcare settings.

Start here to discover clear, engaging explanations of core architectures—CNNs, RNNs, transformers—and how they solve real biomedical challenges. The book weaves compelling case studies and practical workflows that translate complex theory into actionable pipelines: from preprocessing and model selection to evaluation, interpretability, and deployment in clinical environments. It also addresses critical topics like explainable AI, data privacy, bias mitigation, and regulatory considerations that matter to hospitals, biotech firms, and public-health agencies in North America, Europe, Asia-Pacific, and beyond.

Readers will gain concrete skills to design robust models, validate results with clinical rigor, and integrate AI into healthcare systems responsibly. Whether you’re building diagnostic tools, accelerating genomics analysis, or enhancing patient monitoring, Smita Sharma’s writing delivers clarity, relevance, and forward-looking insights.

For anyone working at the intersection of AI and medicine, this book is an indispensable resource to stay ahead in a rapidly evolving field. Empower your projects with proven approaches and industry-aware guidance—order your copy today and advance your work in biomedical data analysis with confidence.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.