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Analysis of Medical Modalities for Improved Diagnosis in Modern Healthcare [Mīkstie vāki]

Edited by (IIITB, India), Edited by (IIITDM Jabalpur, India)
  • Formāts: Paperback / softback, 324 pages, height x width: 234x156 mm, weight: 500 g, 55 Tables, black and white; 85 Line drawings, black and white; 14 Halftones, black and white; 99 Illustrations, black and white
  • Izdošanas datums: 08-Oct-2024
  • Izdevniecība: CRC Press
  • ISBN-10: 0367705370
  • ISBN-13: 9780367705374
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  • Mīkstie vāki
  • Cena: 61,21 €
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Analysis of Medical Modalities for Improved Diagnosis in Modern HealthcarePalielināt
  • Formāts: Paperback / softback, 324 pages, height x width: 234x156 mm, weight: 500 g, 55 Tables, black and white; 85 Line drawings, black and white; 14 Halftones, black and white; 99 Illustrations, black and white
  • Izdošanas datums: 08-Oct-2024
  • Izdevniecība: CRC Press
  • ISBN-10: 0367705370
  • ISBN-13: 9780367705374
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780367705374.html
Keywords:
This book emphasizes the real-time challenges in medical modalities for variety of applications for analysis, classification and identification of different states for improvement of healthcare systems. Each chapter starts with the introduction, need and motivation of the medical modality and covers applications, alongwith real-time case studies.

In modern healthcare, various medical modalities play an important role in improving the diagnostic performance in healthcare systems for various applications, such as prosthesis design, surgical implant design, diagnosis and prognosis, and detection of abnormalities in the treatment of various diseases. Analysis of Medical Modalities for Improved Diagnosis in Modern Healthcare discusses the uses of analysis, modeling, and manipulation of modalities, such as EEG, ECG, EMG, PCG, EOG, MRI, and FMRI, for an automatic identification, classification, and diagnosis of different types of disorders and physiological states. The analysis and applications for post-processing and diagnosis are much-needed topics for researchers and faculty members all across the world in the field of automated and efficient diagnosis using medical modalities. To meet this need, this book emphasizes real-time challenges in medical modalities for a variety of applications for analysis, classification, identification, and diagnostic processes of healthcare systems. Each chapter starts with the introduction, need and motivation of the medical modality, and a number of applications for the identification and improvement of healthcare systems. The chapters can be read independently or consecutively by research scholars, graduate students, faculty members, and practicing scientists who wish to explore various disciplines of healthcare systems, such as computer sciences, medical sciences, and biomedical engineering.

This book aims to improve the direction of future research and strengthen research efforts of healthcare systems through analysis of behavior, concepts, principles, and case studies. This book also aims to overcome the gap between usage of medical modalities and healthcare systems. Several novel applications of medical modalities have been unlocked in recent years, therefore new applications, challenges, and solutions for healthcare systems are the focus of this book.

1. Classification of Alertness and Drowsiness States using the Complex
Wavelet Transform based Approach for EEG Records.
2. Stochastic Event
Synchrony based on a Modified Sparse Bump Modeling: Application to PTSD EEG
Signals.
3. HealFavor: A Chatbot Application in Healthcare.
4. Diagnosis of
Neuromuscular Disorders using Machine Learning Techniques.
5. Prosthesis
control using undersampled surface electromyographic signals.
6. Title of
chapter:Assessment and Diagnostic Methods for Coronavirus Disease 2019
(COVID-19).
7. Predictive Analysis of Breast Cancer using Infrared Images
with Machine Learning Algorithms.
8. Histopathological Image Analysis and
Classification Techniques for Breast Cancer Detection.
9. Study of Emotional
Intelligence & Neuro-Fuzzy System.
10. Essential Statistical Tools for
Analysis of Brain Computer Interface.
11. Brain Computer Interfaces: The
basics, state of the art and future. 12.  Oriented Approaches for Brain
Computing and Human Behavior Computing Using Machine Learning.
13. An
Automated Diagnosis System for Cardiac Arrhythmia Classification.
Varun Bajaj is working as a faculty in the discipline of Electronics and Communication Engineering, at Indian Institute of Information Technology, Design and Manufacturing (IIITDM) Jabalpur, India.

G R Sinha is an Adjunct Professor at International Institute of Information Technology Bangalore (IIITB) and currently deputed as Professor at Myanmar Institute of Information Technology (MIIT) Mandalay Myanmar.