Computer-Assisted Analysis for Digital Medicinal Imagery

Amit Sinha

ISBN 13: 9798369352267

Published by IGI Global, 2024

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - The constantly evolving healthcare industry has experienced tremendous technological advancements that have significantly revolutionized medical imaging. However, with the increasing volume and complexity of medical image data, existing analysis methods must also be updated to be efficient and accurate. This is where the challenge lies-a need for a comprehensive solution that bridges the gap between cutting-edge technology and effective healthcare delivery. Computer-Assisted Analysis for Digital Medicinal Imagery offers a roadmap for navigating the intricate landscape of digital medicinal imagery analysis. Unlocking the power of machine learning and breaking down the basics provides researchers, clinicians, and students with the tools necessary to harness technology and improve healthcare outcomes.

Seller Inventory # 9798369352267

Bibliographic Details

• Title: Computer-Assisted Analysis for Digital Medicinal Imagery
• Publisher: IGI Global
• Publication Date: 2024
• Language: English
• ISBN 13: 9798369352267
• Binding: Buch
• Condition: Neu
• Dimensions: 260W x 183H x 32D millimeters 260 millimeters width by 183 millimeters height by 32 millimeters depth
• Weight: 1148 grams

About this title

Synopsis

The constantly evolving healthcare industry has experienced tremendous technological advancements that have significantly revolutionized medical imaging. However, with the increasing volume and complexity of medical image data, existing analysis methods must also be updated to be efficient and accurate. This is where the challenge lies-a need for a comprehensive solution that bridges the gap between cutting-edge technology and effective healthcare delivery. Computer-Assisted Analysis for Digital Medicinal Imagery offers a roadmap for navigating the intricate landscape of digital medicinal imagery analysis. Unlocking the power of machine learning and breaking down the basics provides researchers, clinicians, and students with the tools necessary to harness technology and improve healthcare outcomes.

About the Authors

Pranshu Saxena received the B. E. in (2010), M. Tech. (2013), and PhD in 2023. He is currently an Assistant Professor in the Department of Information Technology at the ABES Engineering College, Ghaziabad, India. He has published 22 research articles which include 4-SCI, 6-Scopus, and 12 peer-review journals/conferences. His research interests include medical image processing, automated image segmentation, Image texture analysis, intelligent systems and Pattern Classification. He has been a designated reviewer (ISIEA 2012-2013, IJERT) and committee member (IJACR) for many international conferences and journals in image processing and medical imaging. He has published one book published by LAP Lambert Academic Publishing, USA.

Dr. Sanjay Kumar Singh is currently working as an assistant professor in university school of automation and Robotics, Guru Gobind Singh Indraprastha University, East Delhi campus, Delhi. He has more than 12 years of experience in teaching and received integrated post graduate (B. Tech + M. Tech) degree from ABV-Indian Institute of Information Technology & Management, Gwalior and Ph. D. degree from IKG-Punjab Technical University. He has published more than 50 papers in reputed journals and international conferences. His area of research in the field of machine learning, deep learning, soft computing and intelligent systems.