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9798369352267: Computer-Assisted Analysis for Digital Medicinal Imagery

Sinopsis

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.

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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. Aniket Mahanti is a researcher, educator, and award-winning author who brings over 15 years of experience designing, measuring, analyzing, and modelling complex distributed systems. Currently, he is a Senior Lecturer (Rest of the world: Associate Professor) of Computer Science at The University of Auckland. He received his doctorate in computer science from the University of Calgary, Canada, in 2012. Between 2006 and 2008, he worked as a Research Associate with the Computer Networks group at the University of Calgary. His expertise spans multiple domains such as computer science, social sciences, and business. His post-secondary education was fully funded by prestigious scholarships like the national Natural Sciences and Engineering Research Council of Canada (NSERC) Canada Graduate Scholarship (CGS), and the provincial Alberta Innovates Technology Futures (AITF) graduate student scholarship. He received the Best Paper Award at the International Conference on Smart and Sustainable Agriculture (SSA) 2021. His area of interest is social computing, performance evaluation, data science and cyber security.

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.

Dr. Harikesh Singh is a Senior IEEE Member and working as Associate Professor in Department of Computer Science and Engineering at JSS Academy of Technical Education, Noida with 17 years of teaching experience since 1st Jul, 2022. He has done BTech in 2004, ME in 2007, and PhD in 2015 with specialization of Grid and Cloud Computing. Earlier, he has served as Assistant Professor in CSE Department at Jaypee University of Engineering and Technology, Guna, Madhya Pradesh for 12 Years and afterwards he has worked at ABES Engineering College, Ghaziabad for 3 years as Associate Professor in IT Department. He has supervised 4 MTech Students and 1 PhD Student in the area of Grid and Cloud Computing. He has published 7 SCI, 22 Scopus indexed journal and conference papers till the date. Currently, he is working in the field of Machine Learning and AI with IoT and Blockchain Technology. He has 10 published patents and organised many faculty development programs.

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Sinha, Amit (EDT); Saxena, Pranshu (EDT); Singh, Sanjay Kumar (EDT); Singh, Harikesh (EDT)
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Sinha, Amit (EDT); Saxena, Pranshu (EDT); Singh, Sanjay Kumar (EDT); Singh, Harikesh (EDT)
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Sinha, Amit (EDT); Saxena, Pranshu (EDT); Singh, Sanjay Kumar (EDT); Singh, Harikesh (EDT)
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Sinha, Amit (EDT); Saxena, Pranshu (EDT); Singh, Sanjay Kumar (EDT); Singh, Harikesh (EDT)
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Hardcover. Condición: new. Hardcover. 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 liesa 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. This book takes a holistic approach to tackling the challenges of digital medicinal imagery analysis, covering practical topics such as imaging modalities, machine learning applications, and emerging technologies. By equipping readers with the knowledge and tools needed to overcome these obstacles, it lays the foundation for a future where healthcare delivery is not just efficient, but transformative. Offers a roadmap for navigating the intricate landscape of digital medicinal imagery analysis. This book takes a holistic approach to tackling the challenges of digital medicinal imagery analysis, covering practical topics such as imaging modalities, machine learning applications, and emerging technologies. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9798369352267

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Amit Sinha
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Hardcover. Condición: new. Hardcover. 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 liesa 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. This book takes a holistic approach to tackling the challenges of digital medicinal imagery analysis, covering practical topics such as imaging modalities, machine learning applications, and emerging technologies. By equipping readers with the knowledge and tools needed to overcome these obstacles, it lays the foundation for a future where healthcare delivery is not just efficient, but transformative. Offers a roadmap for navigating the intricate landscape of digital medicinal imagery analysis. This book takes a holistic approach to tackling the challenges of digital medicinal imagery analysis, covering practical topics such as imaging modalities, machine learning applications, and emerging technologies. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9798369352267

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Amit Sinha
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Hardcover. Condición: new. Hardcover. 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 liesa 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. This book takes a holistic approach to tackling the challenges of digital medicinal imagery analysis, covering practical topics such as imaging modalities, machine learning applications, and emerging technologies. By equipping readers with the knowledge and tools needed to overcome these obstacles, it lays the foundation for a future where healthcare delivery is not just efficient, but transformative. Offers a roadmap for navigating the intricate landscape of digital medicinal imagery analysis. This book takes a holistic approach to tackling the challenges of digital medicinal imagery analysis, covering practical topics such as imaging modalities, machine learning applications, and emerging technologies. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Nº de ref. del artículo: 9798369352267

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