Emerging Trends and Applications of Deep Learning for Biomedical Data Analysis

Sharma, Smita (EDT)/ Balusamy, Balamurugan (EDT)/ Ramesh, S. (EDT)

Academic Press Inc（2025/06発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 232 p.
• 言語 ENG
• 商品コード 9780443267659
• DDC分類 610.285631

Full 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.

Contents

1. Deep learning, artificial intelligence, and bioinformatics promises innovations and imminent forecasts in SARS-COVID-19 genome data analysis
2. Integration of IoT and AI for potato leaf disease detection: enhancing agricultural efficiency and sustainability
3. A hybridized long-short-term memory networks-based deep learning model using reptile search optimization for COVID-19 prediction
4. Improving coronavirus classification accuracy with transfer learning and chest radiograph analysis
5. A hybrid deep neural network using the Levenberg-Marquardt algorithm applied to the nonlinear magnetohydrodynamic Jeffery-Hamel blood flow problem
6. An image segmentation method using intuitionistic fuzzy k-means and convolutional neural networks in multiclass image classification
7. Deep learning for wearable sensor data analysis
8. Unveiling emotions in real-time: a novel approach to face emotion recognition
9. Unleashing the power of convolutional neural networks for diabetic retinopathy detection in ophthalmology
10. Case studies and use cases of deep learning for biomedical applications
11. A convolutional neural network-based deep ensemble method for computed tomography scan image-based lung cancer diagnosis