Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis

A comprehensive guide to the development of classification techniques for computer-aided diagnosis and medical image processing

Nilanjan Dey (Author)

9780128180044, Elsevier Science

Paperback, published 31 July 2019

218 pages
23.4 x 19 x 1.5 cm, 0.45 kg

Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis covers the most current advances on how to apply classification techniques to a wide variety of clinical applications that are appropriate for researchers and biomedical engineers in the areas of machine learning, deep learning, data analysis, data management and computer-aided diagnosis (CAD) systems design. The book covers several complex image classification problems using pattern recognition methods, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Bayesian Networks (BN) and deep learning. Further, numerous data mining techniques are discussed, as they have proven to be good classifiers for medical images.

1. Classification of Unhealthy and Healthy Neonates in Neonatal Intensive Care Units Using Medical Thermography Processing and Artificial Neural Network2. Use of Health-related Indices and Cassification Methods in Medical Data3. Image Analysis for Diagnosis and Early Detection of Hepatoprotective Activity4. Characterization of Stuttering Dysfluencies using Distinctive Prosodic and Source Features5. A Deep Learning Approach for Patch-based Disease Diagnosis from Microscopic Images6. A Breast Tissue Characterization Framework Using PCA and Weighted Score Fusion of Neural Network Classifiers7. Automated Arrhythmia Classification for Monitoring Cardiac Patients Using Machine Learning Techniques8. IoT-based Fluid and Heartbeat Monitoring For Advanced Healthcare

Subject Areas: Biomedical engineering [MQW]