Emerging Trends and Applications of Deep Learning for Biomedical Data Analysis
Published on 2. May 2025
330 pages
978-0-443-26766-6 (ISBN)
Description
Emerging Trends and Applications of Deep Learning for Biomedical Data Analysis introduces the latest emerging trends and applications of deep learning in biomedical data analysis. This book delves into various use cases where deep learning is applied in industrial, social, and personal contexts within the biomedical domain. By gaining a comprehensive understanding of deep learning in biomedical data analysis, readers will develop the skills to critically evaluate research papers, methodologies, and emerging trends. In 11 chapters, this book provides insights into the fundamentals of the latest research trends in the applications of deep learning in biosciences. With several case studies and use cases, it familiarizes the reader with a comprehensive understanding of deep learning algorithms, architectures, and methodologies speci cally applicable to biomedical data analysis. This title is an ideal reference for researchers across the biomedical sciences.
- Provides a succinct overview of the cutting-edge technologies that are altering disease diagnosis, patient monitoring, and medical research
- Bridges the gap between biomedical engineering and deep learning by providing a comprehensive resource for comprehending the intersection of these disciplines
- Investigates how deep learning may change healthcare by providing new insights, diagnostics, and treatments via intelligent biomedical systems
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Smita Sharma | Balamurugan Balusamy | S. Ramesh
Emerging Trends and Applications of Deep Learning for Biomedical Data Analysis
Book
06/2025
Academic Press
€171.50
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Content
1. Deep learning, artificial intelligence, and bioinformatics promises innovations and imminent forecasts in SARS-COVID-19 genome data analysis2. Integration of IoT and AI for potato leaf disease detection: enhancing agricultural efficiency and sustainability3. A hybridized long-short-term memory networks-based deep learning model using reptile search optimization for COVID-19 prediction4. Improving coronavirus classification accuracy with transfer learning and chest radiograph analysis5. A hybrid deep neural network using the Levenberg-Marquardt algorithm applied to the nonlinear magnetohydrodynamic Jeffery-Hamel blood flow problem6. An image segmentation method using intuitionistic fuzzy k-means and convolutional neural networks in multiclass image classification7. Deep learning for wearable sensor data analysis8. Unveiling emotions in real-time: a novel approach to face emotion recognition9. Unleashing the power of convolutional neural networks for diabetic retinopathy detection in ophthalmology10. Case studies and use cases of deep learning for biomedical applications11. A convolutional neural network-based deep ensemble method for computed tomography scan image-based lung cancer diagnosis
