Swarm Optimization for Biomedical Applications

Biomedical engineering is a rapidly growing interdisciplinary area that is providing solutions to biological and medical problems and improving the healthcare system. It is connected to various applications like protein structure prediction, computer-aided drug design, and computerized medical diagnosis based on image and signal data, which accomplish low-cost, accurate, and reliable solutions for improving healthcare services. With the recent advancements, machine learning (ML) and deep learning (DL) techniques are widely used in biomedical engineering to develop intelligent decision-making healthcare systems in real-time. However, accuracy and reliability in model performance can be a concern in tackling data generated from medical images and signals, making it challenging for researchers and practitioners. Therefore, optimized models can produce quality healthcare services to handle the complexities involved in biomedical research. Various optimization techniques have been employed to optimize parameters, hyper-parameters, and architectural information of ML/DL models explicitly applied to biological, medical, and signal data. The swarm intelligence approach has the potential to solve complex non-linear optimization problems. It mimics the collective behavior of social swarms such as ant colonies, honey bees, and bird flocks. The cooperative nature of swarms can search global settings of ML/DL models, which efficiently provide the solution to biomedical engineering applications. Finally, the book aims to provide the utility of swarm optimization and similar optimization techniques to design ML/DL models to improve the solutions related to biomedical engineering.

Table of Contents:
Preface. Introduction. 1. A Swarm Intelligence Optimization for Lung Cancer Detection from RNA-Seq Gene Expression Data Using Convolutional Neural Networks. 2. A Comprehensive Review on Optimization Algorithms and Applications for Medical Imaging Data. 3. A Comprehensive Analysis of Auto Carrier Loading and Cargo Space Optimization for a Specific Type of Container Using GA. 4. Classification of Heart Disease by Using Particle Swarm Optimization for Hyperparameter Tuning of Machine Learning Models. 5. Mathematical Model for Swarm Optimization in Multimodal Biomedical Images. 6. Swarm Intelligence in Lung Cancer Detection and IoT-Enabled Data Transmission: A Technological Approach. 7. Radiant Insights: Unveiling the Future of Cancer Diagnosis through DOTA-NOC and PET-CT Algorithmic Synergy. 8. Swarm-Optimized Pancreatic Precision: Comprehensive Early Detection through Endoscopic Ultrasound Data Analysis. 9. An Overview of Optimization Techniques for Pre-processing of RNA-seq Data. 10. Improved GA based Clustering with a New Selection Method for Categorical Dental Data. 11. DeepExuDetectNet: Diabetic Retinopathy Diagnosis: Blood Vessel Segmentation and Exudates Disease Detection in Fundus Images. 12. Analysis of Human Emotions with Bio-signals (EEG) using Deep CNN. 13. Supervised GAN (SGAN): Method and Application on Labelled Genomic/Epigenomic Data. 14. A Lightweight Attention-based Convolutional Neural Network for Classification of 3D Biomedical Images. 15. Predictive Security Architecture for Securing Medical Images in Cloud Based IoT. Index.