Emerging Trends and Applications of Deep Learning for Biomedical Data Analysis

Smita Sharma, Balamurugan Balusamy, S. Ramesh, Ali Kashif Bashir

• Publisher: Academic Press
• Year: 2025
• eBook ISBN: 9780443267666
• Edition: 0
• Topic: Biological Sciences
• Subtopic: Biology
• Index: Biological Sciences

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. The 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 14 chapters this book provides both insights into the fundamentals as 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 specifically 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

Table of contents

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