Название: Computational Intelligence and Machine Learning Approaches in Biomedical Engineering and Health Care Systems
Автор: P. Naga Srinivasu, Norita Md Norwawi, Sheng Lung Peng
Издательство: Bentham Books
Год: 2022
Страниц: 225
Язык: английский
Формат: pdf (true), epub
Размер: 21.3 MB

Computational Intelligence and Machine Learning Approaches in Biomedical Engineering and Health Care Systems explains the emerging technology that currently drives computer-aided diagnosis, medical analysis and other electronic healthcare systems. 11 book chapters cover advances in biomedical engineering fields achieved through Deep Learning (DL) and soft-computing techniques. Readers are given a fresh perspective of how intelligent systems impact patient outcomes for healthcare professionals who are assisted by advanced computing algorithms.

The Chapter 2 utilizes the Particle Swarm Optimization (PSO) algorithm to effectively segment the microaneurysms. The segmented microaneurysm is analyzed using the measures of Entropy, Skewness, and Kurtosis. The elevated PSO clustering gives high performance irrespective of image contrast. The elevated continuous PSO clustering successfully detects microaneurysms and helps diagnose diabetic retinopathy in the early stage in an efficient way. This work uses digital image processing techniques and mainly concentrates on the effective detection of microaneurysms. The results proved that the proposed approach improves performance in the early detection of diabetic retinopathy. To diagnose the early detection of the non-proliferative diabetic retinopathy, utilizing the PSO clustering for the segmentation of fundus images improves the accuracy of the non-proliferative diabetic retinopathy with Raspberry Pi. The Raspberry Pi is a minimal expense, utilizes open-source programming segments, and proficiently works for constant areas. Raspberry Pi measures the pictures and enhances results. Raspberry Pi improves the nature of the yield and decreases the expense of preparing for clinical picture applications. Raspberry Pi upholds programming dialects like Python, C, C++, Java, and Ruby dialects.

Key Features:

- Covers emerging technologies in biomedical engineering and healthcare that assist physicians in diagnosis, treatment, and surgical planning in a multidisciplinary context
- Provides examples of technical use cases for artificial intelligence, machine learning and deep learning in medicine, with examples of different algorithms
- Introduces readers to the concept of telemedicine and electronic healthcare systems
- Provides implementations of disease prediction models for different diseases including cardiovascular diseases, diabetes and Alzheimer’s disease
- Summarizes key information for learners
- Includes references for advanced readers

The book serves as an essential reference for academic readers, as well as Computer Science enthusiasts who want to familiarize themselves with the practical computing techniques in the field of biomedical engineering (with a focus on medical imaging) and medical informatics.

Contents:
Preface
Convolutional Neural Network for Denoising Left Ventricle Magnetic Resonance Images
Early Diabetic Retinopathy Detection Using Elevated Continuous Particle Swarm Optimization Clustering With Raspberry Pi
E-Health System and Telemedicine: An Overview and its Applications in Health Care and Medicine
Fuzzy Logic Implementation in Patient Monitoring System for Lymphatic Treatment of Leg Pain
Safe Distance and Face Mask Detection using OpenCV and MobileNetV2
Performance Evaluation of ML Algorithms for Disease Prediction Using DWT and EMD Techniques
Cardiovascular Disease Preventive Prediction and Medication (CVDPPM) - A Model Based on AI Techniques for Prediction and Timely Medical Assistance
Personalized Smart Diabetic System Using Hybrid Models of Neural Network Algorithms
A Framework of Smart Mobile Application for Vehicle Health Monitoring
Progression Prediction and Classification of Alzheimer’s Disease using MRI